ant_kernshapeparms = {pol: {'xmax': 0.5 * ant_sizex, 'ymax': 0.5 * ant_sizey,
'rmin': 0.0, 'rmax': 0.5 * ant_diameter,
'rotangle': 0.0} for pol in ['P1', 'P2']}
ant_aprtr = APR.Aperture(pol_type=ant_pol_type, kernel_type=ant_kerntype,
shape=ant_kernshape, parms=ant_kernshapeparms,
lkpinfo=ant_lookupinfo, load_lookup=True)
if identical_antennas:
ant_aprtrs = [ant_aprtr] * n_antennas
# Set up antenna array
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_info[i, 0])), '0', lat, -118.28, ant_info[i, 1:],
f0, nsamples=nts, aperture=ant_aprtrs[i])
ant.f = ant.f0 + DSP.spectax(2 * nts, dt, shift=True)
ants += [ant]
aar = aar + ant
aar.grid(uvspacing=0.4, xypad=2 * NP.max([ant_sizex, ant_sizey]))
antpos_info = aar.antenna_positions(sort=True, centering=True)
# Select time steps
if max_n_timestamps is None:
max_n_timestamps = len(timestamps)
else:
max_n_timestamps = min(max_n_timestamps, len(timestamps))
timestamps = timestamps[min_timestamp:min_timestamp + max_n_timestamps]
master_pb = PGB.ProgressBar(widgets=[PGB.Percentage(),
antpos = antpos[core_ind, :]
ant_info = NP.hstack((antid.reshape(-1, 1), antpos))
n_antennas = ant_info.shape[0]
ant_data = ant_data[:, core_ind, :, :]
with PyCallGraph(output=graphviz, config=config):
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_info[i, 0])),
lat,
ant_info[i, 1:],
f0,
nsamples=nts)
ant.f = ant.f0 + DSP.spectax(2 * nts, dt, shift=True)
ants += [ant]
aar = aar + ant
aar.grid()
antpos_info = aar.antenna_positions(sort=True)
if max_n_timestamps is None:
max_n_timestamps = len(timestamps)
else:
max_n_timestamps = min(max_n_timestamps, len(timestamps))
timestamps = timestamps[:max_n_timestamps]
stand_cable_delays = NP.loadtxt(
PDB.set_trace()
orig_n_antennas = ant_info2.shape[0]
final_n_antennas = NP.round(layout_fraction[ind]*ant_info2.shape[0]).astype(int)
if final_n_antennas <= 1: final_n_antennas = 2
ant_seed = 10
randstate = NP.random.RandomState(ant_seed)
randint = NP.sort(randstate.choice(ant_info2.shape[0], final_n_antennas, replace=False))
ant_info2 = ant_info2[randint,:]
ant_id2 = ant_id2[randint]
n_antennas = ant_info2.shape[0]
for ti in nts:
ants = []
aar = AA.AntennaArray()
for ai in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_id2[ai])), 0.0, ant_info2[ai,:], f0, nsamples=ti)
ant.f = ant.f0 + DSP.spectax(2*ti, 1/(2*ti*freq_resolution), shift=True)
ants += [ant]
aar = aar + ant
aar.grid(xypad=2*NP.max([ant_sizex, ant_sizey]))
du = aar.gridu[0,1] - aar.gridu[0,0]
dv = aar.gridv[1,0] - aar.gridv[0,0]
dxdy = du * dv * (FCNST.c/aar.f.max())**2
if layout.split('-')[0] == 'MWA':
fillfrac = n_antennas*ant_sizex*ant_sizey / (dxdy*aar.gridu.size) * 100
elif layout.split('-')[0] == 'HEX':
fillfrac = n_antennas*NP.pi*(ant_diameter/2)**2 / (dxdy*aar.gridu.size) * 100
for itr in niter:
if layout.split('-')[0] == 'MWA':
fname = '/data3/t_nithyanandan/project_MOFF/simulated/MWA/profiling/FX_serial_all_lines/MOFF_FX_performance_comparison_{0}_{1:.1f}mx{2:.1f}m_{3:0d}x{4:.1f}_kHz_{5:.1f}_MHz_{6:0d}_of_{7:0d}_ant_{8:0d}_acc_{9:0d}_pix_fillfrac_{10:.1f}_r{11:0d}'.format(layout,ant_sizex,ant_sizey,2*ti,freq_resolution/1e3,f0/1e6,n_antennas,orig_n_antennas,itr,aar.gridu.size,fillfrac,rnum)
# antpos -= NP.mean(antpos, axis=0).reshape(1,-1)
core_ind = NP.logical_and((NP.abs(antpos[:, 0]) < max_antenna_radius),
(NP.abs(antpos[:, 1]) < max_antenna_radius))
# core_ind = NP.logical_and((NP.abs(antpos[:,0]) <= NP.max(NP.abs(antpos[:,0]))), (NP.abs(antpos[:,1]) < NP.max(NP.abs(antpos[:,1]))))
ant_info = NP.hstack((antid[core_ind].reshape(-1, 1), antpos[core_ind, :]))
n_antennas = ant_info.shape[0]
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_info[i, 0])),
lat,
ant_info[i, 1:],
f0,
nsamples=nchan)
ant.f = ant.f0 + DSP.spectax(2 * nchan, dt, shift=True)
ants += [ant]
aar = aar + ant
timestamps = hdulist0['TIMESTAMPS'].data['timestamp']
if max_n_timestamps is None:
max_n_timestamps = len(timestamps)
else:
max_n_timestamps = min(max_n_timestamps, len(timestamps))
timestamps = timestamps[:max_n_timestamps]
stand_cable_delays = NP.loadtxt(
'/data3/t_nithyanandan/project_MOFF/data/samples/cable_delays.txt',
skiprows=1)
antennas = stand_cable_delays[:, 0].astype(NP.int).astype(str)
# n_src = 4
# src_flux = NP.ones(n_src)
# skypos = 0.25*NP.hstack((NP.cos(2.0*NP.pi*NP.arange(n_src).reshape(-1,1)/n_src),
# NP.sin(2.0*NP.pi*NP.arange(n_src).reshape(-1,1)/n_src)))
# src_flux = [1.0, 1.0, 1.0, 1.0]
# skypos = NP.asarray([[0.25, 0.0], [0.0, -0.25], [-0.25, 0.0], [0.0, 0.25]])
# skypos = NP.asarray([[0.0, 0.0], [0.2, 0.0], [0.0, 0.4], [0.0, -0.5]])
nvect = NP.sqrt(1.0-NP.sum(skypos**2, axis=1)).reshape(-1,1)
skypos = NP.hstack((skypos,nvect))
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_info[i,0])), lat, ant_info[i,1:], f0, nsamples=nchan/2)
ant.f = ant.f0 + DSP.spectax(nchan, dt, shift=True)
ants += [ant]
aar = aar + ant
iar = AA.InterferometerArray(antenna_array=aar)
iar.grid()
antpos_info = aar.antenna_positions(sort=True)
Ef_runs = None
count = 0
for i in xrange(itr):
E_timeseries_dict = SIM.stochastic_E_timeseries(f_center, nchan/2, 2*channel_width,
flux_ref=src_flux, skypos=skypos,
antpos=antpos_info['positions'],
def main():
parser = argparse.ArgumentParser(description='Program to compare performance of MOFF and FX codes')
parser.add_argument('--nts', help='Number of time samples per stream', dest='nts', default=8, type=int, metavar='nts')
parser.add_argument('--xmax', help='Farthest x-location of antenna from core', dest='xmax', default=None, type=float, metavar='xmax', required=False)
parser.add_argument('--ymax', help='Farthest y-location of antenna from core', dest='ymax', default=None, type=float, metavar='ymax', required=False)
parser.add_argument('--ant-diameter', help='Antenna diameter (m)', dest='ant_diameter', default=None, type=float, metavar='ant_diameter', required=False)
parser.add_argument('--ant-sizex', help='Antenna x-size (m)', dest='ant_sizex', default=None, type=float, metavar='ant_sizex', required=False)
parser.add_argument('--ant-sizey', help='Antenna x-size (m)', dest='ant_sizey', default=None, type=float, metavar='ant_sizey', required=False)
parser.add_argument('--max-nt', help='Maximum number of time stamps', dest='ntmax', default=4, type=int, metavar='ntmax')
parser.add_argument('--layout', help='Antenna array layout', dest='layout', default=None, type=str, metavar='layout', required=False)
parser.add_argument('--layout-fraction', help='Fraction of original antenna array layout', dest='layout_fraction', default=1.0, type=float, metavar='layout_fraction', required=False)
parser.add_argument('--layout-file', help='Antenna array layout file', dest='layout_file', default=None, type=str, metavar='layout_file', required=False)
args = vars(parser.parse_args())
xmax = args['xmax']
ymax = args['ymax']
ant_sizex = args['ant_sizex']
ant_sizey = args['ant_sizey']
ant_diameter = args['ant_diameter']
max_n_timestamps = args['ntmax']
nts = args['nts']
array_layout = args['layout']
layout_fraction = args['layout_fraction']
layout_file = args['layout_file']
if (layout_fraction <= 0.0) or (layout_fraction > 1.0):
raise ValueError('Fraction of original layout has to lie in the range 0-1')
# Antenna initialization
# lat = -26.701 # Latitude of MWA in degrees
lat = -30.7224 # Latitude of HERA in degrees
f0 = 150e6 # Center frequency
nchan = 2 * nts # number of frequency channels, factor 2 for padding before FFT
identical_antennas = True
if array_layout.split('-')[0] == 'MWA':
ant_locs, ant_id = AL.MWA_128T(layout_file)
ant_info = ant_locs - NP.mean(ant_locs, axis=0, keepdims=True)
if (xmax is not None) and (ymax is not None):
if (xmax < 160.0) and (ymax < 160.0):
core_ind1 = NP.logical_and((NP.abs(ant_info[:,0]) < 160.0), (NP.abs(ant_info[:,1]) < 160.0))
else:
core_ind1 = NP.logical_and((NP.abs(ant_info[:,0]) < 600.0), (NP.abs(ant_info[:,1]) < 600.0))
ant_info1 = ant_info[core_ind1,:]
ant_info1 = ant_info1 - NP.mean(ant_info1, axis=0, keepdims=True)
ant_id1 = ant_id[core_ind1]
else:
ant_info1 = NP.copy(ant_info)
ant_id1 = NP.copy(ant_id)
nx = 4 # dipoles along x
ny = 4 # dipoles along y
dx = 1.1 # dipole spacing along x
dy = 1.1 # dipole spacing along y
ant_sizex = nx * dx
ant_sizey = ny * dy
ant_diameter = NP.sqrt(ant_sizex**2 + ant_sizey**2)
ant_kernshape = {pol: 'rect' for pol in ['P1','P2']}
bl_kernshape = {pol: 'auto_convolved_rect' for pol in ['P11','P12','P21','P22']}
elif array_layout.split('-')[0] == 'HEX':
ant_locs, ant_id = AL.hexagon_generator(ant_diameter, n_total=int(array_layout.split('-')[1]))
ant_diameter = ant_diameter
ant_sizex = ant_diameter
ant_sizey = ant_diameter
ant_kernshape = {pol: 'circular' for pol in ['P1','P2']}
bl_kernshape = {pol: 'auto_convolved_circular' for pol in ['P11','P12','P21','P22']}
ant_info1 = NP.copy(ant_locs)
ant_id1 = NP.copy(ant_id)
else:
raise ValueError('Other antenna array layouts not supported at the moment')
core_ind2 = (NP.abs(ant_info1[:,0]) <= xmax) & (NP.abs(ant_info1[:,1]) <= ymax)
ant_info2 = ant_info1[core_ind2,:]
ant_info2 = ant_info2 - NP.mean(ant_info2, axis=0, keepdims=True)
ant_id2 = ant_id1[core_ind2]
orig_n_antennas = ant_info2.shape[0]
final_n_antennas = NP.round(layout_fraction*ant_info2.shape[0]).astype(int)
if final_n_antennas <= 1: final_n_antennas = 2
ant_seed = 10
randstate = NP.random.RandomState(ant_seed)
randint = NP.sort(randstate.choice(ant_info2.shape[0], final_n_antennas, replace=False))
ant_info2 = ant_info2[randint,:]
ant_id2 = ant_id2[randint]
n_antennas = ant_info2.shape[0]
f_center = f0
channel_width = 40e3
bandwidth = nchan * channel_width
dt = 1/bandwidth
dts = 1/channel_width
timestamps = 2*dts * NP.arange(max_n_timestamps)
MOFF_tbinsize = None
FX_tbinsize = None
src_seed = 50
rstate = NP.random.RandomState(src_seed)
NP.random.seed(src_seed)
# n_src = 1
# lmrad = 0.0*NP.ones(n_src)
# lmang = NP.zeros(n_src)
n_src = 10
lmrad = rstate.uniform(low=0.0, high=0.2, size=n_src).reshape(-1,1)
lmang = rstate.uniform(low=0.0, high=2*NP.pi, size=n_src).reshape(-1,1)
skypos = NP.hstack((lmrad * NP.cos(lmang), lmrad * NP.sin(lmang))).reshape(-1,2)
skypos = NP.hstack((skypos, NP.sqrt(1.0-(skypos[:,0]**2 + skypos[:,1]**2)).reshape(-1,1)))
src_flux = 10.0*NP.ones(n_src)
grid_map_method = 'sparse'
# grid_map_method = 'regular'
ant_pol_type = 'dual'
ant_kerntype = {pol: 'func' for pol in ['P1','P2']}
ant_lookupinfo = None
# ant_kerntype = {pol: 'lookup' for pol in ['P1','P2']}
# ant_kernshape = None
# ant_lookupinfo = {pol: '/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt' for pol in ['P1','P2']}
ant_kernshapeparms = {pol: {'xmax':0.5*ant_sizex, 'ymax':0.5*ant_sizey, 'rmin': 0.0, 'rmax': 0.5*ant_diameter, 'rotangle':0.0} for pol in ['P1','P2']}
bl_pol_type = 'cross'
bl_kerntype = {pol: 'func' for pol in ['P11','P12','P21','P22']}
bl_lookupinfo = None
# bl_kerntype = {pol: 'lookup' for pol in ['P11','P12','P21','P22']}
# bl_kernshape = None
# bl_lookupinfo = {pol:'/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt' for pol in ['P11','P12','P21','P22']}
bl_kernshapeparms = {pol: {'xmax':0.5*ant_sizex, 'ymax':0.5*ant_sizey, 'rmax': 0.5*ant_diameter, 'rotangle':0.0} for pol in ['P11','P12','P21','P22']}
ant_aprtr = APR.Aperture(pol_type=ant_pol_type, kernel_type=ant_kerntype,
shape=ant_kernshape, parms=ant_kernshapeparms,
lkpinfo=ant_lookupinfo, load_lookup=True)
bl_aprtr = APR.Aperture(pol_type=bl_pol_type, kernel_type=bl_kerntype,
shape=bl_kernshape, parms=bl_kernshapeparms,
lkpinfo=bl_lookupinfo, load_lookup=True)
if identical_antennas:
ant_aprtrs = [ant_aprtr] * n_antennas
# config = Config(max_depth=5, groups=True)
# graphviz = GraphvizOutput(output_file='/data3/t_nithyanandan/project_MOFF/simulated/MWA/figures/profile_graph_{0:0d}x{1:.1f}_kHz_{2:.1f}_MHz_{3:0d}_ant_{4:0d}_acc.png'.format(nchan, channel_width/1e3, f0/1e6, n_antennas, max_n_timestamps))
# config.trace_filter = GlobbingFilter(include=['antenna_array.*'])
# with PyCallGraph(output=graphviz, config=config):
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_id2[i])), lat, ant_info2[i,:], f0, nsamples=nts, aperture=ant_aprtrs[i])
ant.f = ant.f0 + DSP.spectax(2*nts, dt, shift=True)
ants += [ant]
aar = aar + ant
aar.grid(xypad=2*NP.max([ant_sizex, ant_sizey]))
antpos_info = aar.antenna_positions(sort=True, centering=True)
efimgmax = []
for i in xrange(max_n_timestamps):
E_timeseries_dict = SIM.stochastic_E_timeseries(f_center, nchan/2, 2*channel_width,
flux_ref=src_flux, skypos=skypos,
antpos=antpos_info['positions'],
tshift=False)
# ts = Time.now()
# timestamp = ts.gps
timestamp = timestamps[i]
update_info = {}
update_info['antennas'] = []
update_info['antenna_array'] = {}
update_info['antenna_array']['timestamp'] = timestamp
print 'Consolidating Antenna updates...'
progress = PGB.ProgressBar(widgets=[PGB.Percentage(), PGB.Bar(marker='-', left=' |', right='| '), PGB.Counter(), '/{0:0d} Antennas '.format(n_antennas), PGB.ETA()], maxval=n_antennas).start()
antnum = 0
for label in aar.antennas:
adict = {}
adict['label'] = label
adict['action'] = 'modify'
adict['timestamp'] = timestamp
ind = antpos_info['labels'].index(label)
adict['t'] = E_timeseries_dict['t']
adict['gridfunc_freq'] = 'scale'
adict['gridmethod'] = 'NN'
adict['distNN'] = 3.0
adict['tol'] = 1.0e-6
adict['maxmatch'] = 1
adict['Et'] = {}
adict['flags'] = {}
adict['stack'] = True
adict['wtsinfo'] = {}
for pol in ['P1', 'P2']:
adict['flags'][pol] = False
adict['Et'][pol] = E_timeseries_dict['Et'][:,ind]
# adict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt'}]
adict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
update_info['antennas'] += [adict]
progress.update(antnum+1)
antnum += 1
progress.finish()
aar.update(update_info, parallel=True, verbose=True)
if grid_map_method == 'regular':
aar.grid_convolve_new(pol='P1', method='NN', distNN=0.5*NP.sqrt(ant_sizex**2+ant_sizey**2), identical_antennas=False, cal_loop=False, gridfunc_freq='scale', wts_change=False, parallel=False, pp_method='pool')
else:
if i == 0:
aar.genMappingMatrix(pol='P1', method='NN', distNN=0.5*NP.sqrt(ant_sizex**2+ant_sizey**2), identical_antennas=True, gridfunc_freq='scale', wts_change=False, parallel=False)
if i == 0:
efimgobj = AA.NewImage(antenna_array=aar, pol='P1')
else:
efimgobj.update(antenna_array=aar, reset=True)
efimgobj.imagr(pol='P1', weighting='natural', pad=0, stack=True, grid_map_method=grid_map_method, cal_loop=False)
efimgobj.accumulate(tbinsize=MOFF_tbinsize)
efimgobj.removeAutoCorr(forceeval=True, datapool='avg', pad=0)
avg_efimg = efimgobj.nzsp_img_avg['P1']
if avg_efimg.ndim == 4:
avg_efimg = avg_efimg[0,:,:,:]
beam_MOFF = efimgobj.nzsp_beam_avg['P1']
if beam_MOFF.ndim == 4:
beam_MOFF = beam_MOFF[0,:,:,:]
img_rms_MOFF = NP.std(NP.mean(avg_efimg, axis=2))
beam_rms_MOFF = NP.std(NP.mean(beam_MOFF, axis=2))
img_max_MOFF = NP.max(NP.mean(avg_efimg, axis=2))
# Begin interferometry FX processing
iar = AA.InterferometerArray(antenna_array=aar)
for bllabels in iar.interferometers:
iar.interferometers[bllabels].aperture = copy.deepcopy(bl_aprtr)
iar.refresh_antenna_pairs()
iar.stack(on_flags=True, on_data=True, parallel=False, nproc=None)
tbinsize = None
iar.accumulate(tbinsize=tbinsize)
interferometer_level_update_info = {}
interferometer_level_update_info['interferometers'] = []
for label in iar.interferometers:
idict = {}
idict['label'] = label
idict['timestamp'] = timestamp
idict['action'] = 'modify'
idict['gridfunc_freq'] = 'scale'
idict['gridmethod'] = 'NN'
idict['distNN'] = 0.5 * FCNST.c / f0
idict['tol'] = 1.0e-6
idict['maxmatch'] = 1
idict['wtsinfo'] = {}
for pol in ['P11', 'P12', 'P21', 'P22']:
idict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
interferometer_level_update_info['interferometers'] += [idict]
iar.update(antenna_level_updates=None, interferometer_level_updates=interferometer_level_update_info, do_correlate=None, parallel=True, verbose=True)
iar.grid(uvpad=2*NP.max([ant_sizex, ant_sizey]))
if grid_map_method == 'regular':
iar.grid_convolve_new(pol='P11', method='NN', distNN=NP.sqrt(ant_sizex**2+ant_sizey**2), identical_interferometers=True, gridfunc_freq='scale', wts_change=False, parallel=False, pp_method='pool')
else:
iar.genMappingMatrix(pol='P11', method='NN', distNN=NP.sqrt(ant_sizex**2+ant_sizey**2), identical_interferometers=True, gridfunc_freq='scale', wts_change=False, parallel=False)
vfimgobj = AA.NewImage(interferometer_array=iar, pol='P11')
vfimgobj.imagr(pol='P11', weighting='natural', pad=0, grid_map_method=grid_map_method)
randstate.choice(ant_info2.shape[0],
final_n_antennas,
replace=False))
ant_info2 = ant_info2[randint, :]
ant_id2 = ant_id2[randint]
n_antennas = ant_info2.shape[0]
for ti in nts:
ants = []
aar = AA.AntennaArray()
for ai in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_id2[ai])),
0.0,
ant_info2[ai, :],
f0,
nsamples=ti)
ant.f = ant.f0 + DSP.spectax(
2 * ti, 1 / (2 * ti * freq_resolution), shift=True)
ants += [ant]
aar = aar + ant
aar.grid(xypad=2 * NP.max([ant_sizex, ant_sizey]))
du = aar.gridu[0, 1] - aar.gridu[0, 0]
dv = aar.gridv[1, 0] - aar.gridv[0, 0]
dxdy = du * dv * (FCNST.c / aar.f.max())**2
if layout.split('-')[0] == 'MWA':
fillfrac = n_antennas * ant_sizex * ant_sizey / (
dxdy * aar.gridu.size) * 100
elif layout.split('-')[0] == 'HEX':
fillfrac = n_antennas * NP.pi * (ant_diameter / 2)**2 / (
dxdy * aar.gridu.size) * 100
for itr in niter:
def main():
parser = argparse.ArgumentParser(
description='Program to compare performance of MOFF and FX codes')
parser.add_argument('--nts',
help='Number of time samples per stream',
dest='nts',
default=8,
type=int,
metavar='nts')
parser.add_argument('--xmax',
help='Farthest x-location of antenna from core',
dest='xmax',
default=None,
type=float,
metavar='xmax',
required=False)
parser.add_argument('--ymax',
help='Farthest y-location of antenna from core',
dest='ymax',
default=None,
type=float,
metavar='ymax',
required=False)
parser.add_argument('--ant-diameter',
help='Antenna diameter (m)',
dest='ant_diameter',
default=None,
type=float,
metavar='ant_diameter',
required=False)
parser.add_argument('--ant-sizex',
help='Antenna x-size (m)',
dest='ant_sizex',
default=None,
type=float,
metavar='ant_sizex',
required=False)
parser.add_argument('--ant-sizey',
help='Antenna x-size (m)',
dest='ant_sizey',
default=None,
type=float,
metavar='ant_sizey',
required=False)
parser.add_argument('--max-nt',
help='Maximum number of time stamps',
dest='ntmax',
default=4,
type=int,
metavar='ntmax')
parser.add_argument('--layout',
help='Antenna array layout',
dest='layout',
default=None,
type=str,
metavar='layout',
required=False)
parser.add_argument('--layout-fraction',
help='Fraction of original antenna array layout',
dest='layout_fraction',
default=1.0,
type=float,
metavar='layout_fraction',
required=False)
parser.add_argument('--layout-file',
help='Antenna array layout file',
dest='layout_file',
default=None,
type=str,
metavar='layout_file',
required=False)
args = vars(parser.parse_args())
xmax = args['xmax']
ymax = args['ymax']
ant_sizex = args['ant_sizex']
ant_sizey = args['ant_sizey']
ant_diameter = args['ant_diameter']
max_n_timestamps = args['ntmax']
nts = args['nts']
array_layout = args['layout']
layout_fraction = args['layout_fraction']
layout_file = args['layout_file']
if (layout_fraction <= 0.0) or (layout_fraction > 1.0):
raise ValueError(
'Fraction of original layout has to lie in the range 0-1')
# Antenna initialization
# lat = -26.701 # Latitude of MWA in degrees
lat = -30.7224 # Latitude of HERA in degrees
f0 = 150e6 # Center frequency
nchan = 2 * nts # number of frequency channels, factor 2 for padding before FFT
identical_antennas = True
if array_layout.split('-')[0] == 'MWA':
ant_locs, ant_id = AL.MWA_128T(layout_file)
ant_info = ant_locs - NP.mean(ant_locs, axis=0, keepdims=True)
if (xmax is not None) and (ymax is not None):
if (xmax < 160.0) and (ymax < 160.0):
core_ind1 = NP.logical_and((NP.abs(ant_info[:, 0]) < 160.0),
(NP.abs(ant_info[:, 1]) < 160.0))
else:
core_ind1 = NP.logical_and((NP.abs(ant_info[:, 0]) < 600.0),
(NP.abs(ant_info[:, 1]) < 600.0))
ant_info1 = ant_info[core_ind1, :]
ant_info1 = ant_info1 - NP.mean(ant_info1, axis=0, keepdims=True)
ant_id1 = ant_id[core_ind1]
else:
ant_info1 = NP.copy(ant_info)
ant_id1 = NP.copy(ant_id)
nx = 4 # dipoles along x
ny = 4 # dipoles along y
dx = 1.1 # dipole spacing along x
dy = 1.1 # dipole spacing along y
ant_sizex = nx * dx
ant_sizey = ny * dy
ant_diameter = NP.sqrt(ant_sizex**2 + ant_sizey**2)
ant_kernshape = {pol: 'rect' for pol in ['P1', 'P2']}
bl_kernshape = {
pol: 'auto_convolved_rect'
for pol in ['P11', 'P12', 'P21', 'P22']
}
elif array_layout.split('-')[0] == 'HEX':
ant_locs, ant_id = AL.hexagon_generator(
ant_diameter, n_total=int(array_layout.split('-')[1]))
ant_diameter = ant_diameter
ant_sizex = ant_diameter
ant_sizey = ant_diameter
ant_kernshape = {pol: 'circular' for pol in ['P1', 'P2']}
bl_kernshape = {
pol: 'auto_convolved_circular'
for pol in ['P11', 'P12', 'P21', 'P22']
}
ant_info1 = NP.copy(ant_locs)
ant_id1 = NP.copy(ant_id)
else:
raise ValueError(
'Other antenna array layouts not supported at the moment')
core_ind2 = (NP.abs(ant_info1[:, 0]) <= xmax) & (NP.abs(ant_info1[:, 1]) <=
ymax)
ant_info2 = ant_info1[core_ind2, :]
ant_info2 = ant_info2 - NP.mean(ant_info2, axis=0, keepdims=True)
ant_id2 = ant_id1[core_ind2]
orig_n_antennas = ant_info2.shape[0]
final_n_antennas = NP.round(layout_fraction *
ant_info2.shape[0]).astype(int)
if final_n_antennas <= 1: final_n_antennas = 2
ant_seed = 10
randstate = NP.random.RandomState(ant_seed)
randint = NP.sort(
randstate.choice(ant_info2.shape[0], final_n_antennas, replace=False))
ant_info2 = ant_info2[randint, :]
ant_id2 = ant_id2[randint]
n_antennas = ant_info2.shape[0]
f_center = f0
channel_width = 40e3
bandwidth = nchan * channel_width
dt = 1 / bandwidth
dts = 1 / channel_width
timestamps = 2 * dts * NP.arange(max_n_timestamps)
MOFF_tbinsize = None
FX_tbinsize = None
src_seed = 50
rstate = NP.random.RandomState(src_seed)
NP.random.seed(src_seed)
# n_src = 1
# lmrad = 0.0*NP.ones(n_src)
# lmang = NP.zeros(n_src)
n_src = 10
lmrad = rstate.uniform(low=0.0, high=0.2, size=n_src).reshape(-1, 1)
lmang = rstate.uniform(low=0.0, high=2 * NP.pi, size=n_src).reshape(-1, 1)
skypos = NP.hstack(
(lmrad * NP.cos(lmang), lmrad * NP.sin(lmang))).reshape(-1, 2)
skypos = NP.hstack(
(skypos,
NP.sqrt(1.0 - (skypos[:, 0]**2 + skypos[:, 1]**2)).reshape(-1, 1)))
src_flux = 10.0 * NP.ones(n_src)
grid_map_method = 'sparse'
# grid_map_method = 'regular'
ant_pol_type = 'dual'
ant_kerntype = {pol: 'func' for pol in ['P1', 'P2']}
ant_lookupinfo = None
# ant_kerntype = {pol: 'lookup' for pol in ['P1','P2']}
# ant_kernshape = None
# ant_lookupinfo = {pol: '/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt' for pol in ['P1','P2']}
ant_kernshapeparms = {
pol: {
'xmax': 0.5 * ant_sizex,
'ymax': 0.5 * ant_sizey,
'rmin': 0.0,
'rmax': 0.5 * ant_diameter,
'rotangle': 0.0
}
for pol in ['P1', 'P2']
}
bl_pol_type = 'cross'
bl_kerntype = {pol: 'func' for pol in ['P11', 'P12', 'P21', 'P22']}
bl_lookupinfo = None
# bl_kerntype = {pol: 'lookup' for pol in ['P11','P12','P21','P22']}
# bl_kernshape = None
# bl_lookupinfo = {pol:'/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt' for pol in ['P11','P12','P21','P22']}
bl_kernshapeparms = {
pol: {
'xmax': 0.5 * ant_sizex,
'ymax': 0.5 * ant_sizey,
'rmax': 0.5 * ant_diameter,
'rotangle': 0.0
}
for pol in ['P11', 'P12', 'P21', 'P22']
}
ant_aprtr = APR.Aperture(pol_type=ant_pol_type,
kernel_type=ant_kerntype,
shape=ant_kernshape,
parms=ant_kernshapeparms,
lkpinfo=ant_lookupinfo,
load_lookup=True)
bl_aprtr = APR.Aperture(pol_type=bl_pol_type,
kernel_type=bl_kerntype,
shape=bl_kernshape,
parms=bl_kernshapeparms,
lkpinfo=bl_lookupinfo,
load_lookup=True)
if identical_antennas:
ant_aprtrs = [ant_aprtr] * n_antennas
# config = Config(max_depth=5, groups=True)
# graphviz = GraphvizOutput(output_file='/data3/t_nithyanandan/project_MOFF/simulated/MWA/figures/profile_graph_{0:0d}x{1:.1f}_kHz_{2:.1f}_MHz_{3:0d}_ant_{4:0d}_acc.png'.format(nchan, channel_width/1e3, f0/1e6, n_antennas, max_n_timestamps))
# config.trace_filter = GlobbingFilter(include=['antenna_array.*'])
# with PyCallGraph(output=graphviz, config=config):
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_id2[i])),
lat,
ant_info2[i, :],
f0,
nsamples=nts,
aperture=ant_aprtrs[i])
ant.f = ant.f0 + DSP.spectax(2 * nts, dt, shift=True)
ants += [ant]
aar = aar + ant
aar.grid(xypad=2 * NP.max([ant_sizex, ant_sizey]))
antpos_info = aar.antenna_positions(sort=True, centering=True)
efimgmax = []
for i in xrange(max_n_timestamps):
E_timeseries_dict = SIM.stochastic_E_timeseries(
f_center,
nchan / 2,
2 * channel_width,
flux_ref=src_flux,
skypos=skypos,
antpos=antpos_info['positions'],
tshift=False)
# ts = Time.now()
# timestamp = ts.gps
timestamp = timestamps[i]
update_info = {}
update_info['antennas'] = []
update_info['antenna_array'] = {}
update_info['antenna_array']['timestamp'] = timestamp
print 'Consolidating Antenna updates...'
progress = PGB.ProgressBar(widgets=[
PGB.Percentage(),
PGB.Bar(marker='-', left=' |', right='| '),
PGB.Counter(), '/{0:0d} Antennas '.format(n_antennas),
PGB.ETA()
],
maxval=n_antennas).start()
antnum = 0
for label in aar.antennas:
adict = {}
adict['label'] = label
adict['action'] = 'modify'
adict['timestamp'] = timestamp
ind = antpos_info['labels'].index(label)
adict['t'] = E_timeseries_dict['t']
adict['gridfunc_freq'] = 'scale'
adict['gridmethod'] = 'NN'
adict['distNN'] = 3.0
adict['tol'] = 1.0e-6
adict['maxmatch'] = 1
adict['Et'] = {}
adict['flags'] = {}
adict['stack'] = True
adict['wtsinfo'] = {}
for pol in ['P1', 'P2']:
adict['flags'][pol] = False
adict['Et'][pol] = E_timeseries_dict['Et'][:, ind]
# adict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt'}]
adict['wtsinfo'][pol] = [{
'orientation':
0.0,
'lookup':
'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'
}]
update_info['antennas'] += [adict]
progress.update(antnum + 1)
antnum += 1
progress.finish()
aar.update(update_info, parallel=True, verbose=True)
if grid_map_method == 'regular':
aar.grid_convolve_new(pol='P1',
method='NN',
distNN=0.5 *
NP.sqrt(ant_sizex**2 + ant_sizey**2),
identical_antennas=False,
cal_loop=False,
gridfunc_freq='scale',
wts_change=False,
parallel=False,
pp_method='pool')
else:
if i == 0:
aar.genMappingMatrix(pol='P1',
method='NN',
distNN=0.5 *
NP.sqrt(ant_sizex**2 + ant_sizey**2),
identical_antennas=True,
gridfunc_freq='scale',
wts_change=False,
parallel=False)
if i == 0:
efimgobj = AA.NewImage(antenna_array=aar, pol='P1')
else:
efimgobj.update(antenna_array=aar, reset=True)
efimgobj.imagr(pol='P1',
weighting='natural',
pad=0,
stack=True,
grid_map_method=grid_map_method,
cal_loop=False)
efimgobj.accumulate(tbinsize=MOFF_tbinsize)
efimgobj.removeAutoCorr(forceeval=True, datapool='avg', pad=0)
avg_efimg = efimgobj.nzsp_img_avg['P1']
if avg_efimg.ndim == 4:
avg_efimg = avg_efimg[0, :, :, :]
beam_MOFF = efimgobj.nzsp_beam_avg['P1']
if beam_MOFF.ndim == 4:
beam_MOFF = beam_MOFF[0, :, :, :]
img_rms_MOFF = NP.std(NP.mean(avg_efimg, axis=2))
beam_rms_MOFF = NP.std(NP.mean(beam_MOFF, axis=2))
img_max_MOFF = NP.max(NP.mean(avg_efimg, axis=2))
# Begin interferometry FX processing
iar = AA.InterferometerArray(antenna_array=aar)
for bllabels in iar.interferometers:
iar.interferometers[bllabels].aperture = copy.deepcopy(bl_aprtr)
iar.refresh_antenna_pairs()
iar.stack(on_flags=True, on_data=True, parallel=False, nproc=None)
tbinsize = None
iar.accumulate(tbinsize=tbinsize)
interferometer_level_update_info = {}
interferometer_level_update_info['interferometers'] = []
for label in iar.interferometers:
idict = {}
idict['label'] = label
idict['timestamp'] = timestamp
idict['action'] = 'modify'
idict['gridfunc_freq'] = 'scale'
idict['gridmethod'] = 'NN'
idict['distNN'] = 0.5 * FCNST.c / f0
idict['tol'] = 1.0e-6
idict['maxmatch'] = 1
idict['wtsinfo'] = {}
for pol in ['P11', 'P12', 'P21', 'P22']:
idict['wtsinfo'][pol] = [{
'orientation':
0.0,
'lookup':
'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'
}]
interferometer_level_update_info['interferometers'] += [idict]
iar.update(antenna_level_updates=None,
interferometer_level_updates=interferometer_level_update_info,
do_correlate=None,
parallel=True,
verbose=True)
iar.grid(uvpad=2 * NP.max([ant_sizex, ant_sizey]))
if grid_map_method == 'regular':
iar.grid_convolve_new(pol='P11',
method='NN',
distNN=NP.sqrt(ant_sizex**2 + ant_sizey**2),
identical_interferometers=True,
gridfunc_freq='scale',
wts_change=False,
parallel=False,
pp_method='pool')
else:
iar.genMappingMatrix(pol='P11',
method='NN',
distNN=NP.sqrt(ant_sizex**2 + ant_sizey**2),
identical_interferometers=True,
gridfunc_freq='scale',
wts_change=False,
parallel=False)
vfimgobj = AA.NewImage(interferometer_array=iar, pol='P11')
vfimgobj.imagr(pol='P11',
weighting='natural',
pad=0,
grid_map_method=grid_map_method)
