from SSINS import SS
from SSINS import Catalog_Plot as cp
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('obs')
parser.add_argument('inpath')
parser.add_argument('outpath')
args = parser.parse_args()
read_kwargs = {'ant_str': 'cross', 'file_type': 'uvfits'}
MF_kwargs = {
'sig_thresh': 5,
'shape_dict': {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8]
},
'N_thresh': 20
}
ss = SS(obs=args.obs,
inpath=args.inpath,
outpath=args.outpath,
read_kwargs=read_kwargs,
bad_time_indices=[0, -1, -2, -3])
ss.MF_prepare(**MF_kwargs)
cp.MF_plot(ss.MF)
read_paths = {
'data':
arr,
'Nbls':
'%s/arrs/%s_%s_INS_Nbls.npym' % (args.inpath, obs, args.flag_choice),
'freq_array':
'%s/metadata/%s_freq_array.npy' % (args.inpath, obs),
'pols':
'%s/metadata/%s_pols.npy' % (args.inpath, obs),
'vis_units':
'%s/metadata/%s_vis_units.npy' % (args.inpath, obs)
}
ins = INS(obs=obs,
outpath=args.outpath,
flag_choice=args.flag_choice,
read_paths=read_paths)
# ins.data[:, :, :82] = np.ma.masked
# ins.data[:, :, -21:] = np.ma.masked
ins.data_ms = ins.mean_subtract()
ins.counts, ins.bins, ins.sig_thresh = ins.hist_make()
cp.INS_plot(ins, **ms_plot_kwargs)
mf = MF(ins,
shape_dict=shape_dict,
point=args.point,
streak=args.streak,
**mf_kwargs)
for test in args.tests:
getattr(mf, 'apply_%s_test' % test)(args.order)
ins.save()
cp.MF_plot(mf, **ms_plot_kwargs)
vmin=0,
vmax=0.03,
aspect=ss.UV.Nfreqs / ss.UV.Ntimes)
ss.INS.data[:, 0, :82] = np.ma.masked
ss.INS.data[:, 0, -21:] = np.ma.masked
ss.INS.data_ms = ss.INS.mean_subtract(order=1)
ss.MF_prepare(sig_thresh=5, N_thresh=20, shape_dict=shape_dict)
ss.MF.apply_match_test(apply_N_thresh=True, order=1)
ss.INS.data[ss.INS.data > 0.03] = np.ma.masked
ss.INS.data_ms = ss.INS.mean_subtract(order=0)
ss.INS.save()
cp.MF_plot(ss.MF,
ms_vmin=-5,
ms_vmax=5,
vmin=0,
vmax=0.03,
aspect=ss.UV.Nfreqs / ss.UV.Ntimes)
ss.apply_flags(choice='INS', INS=ss.INS)
ss.UV.data_array.mask[:, auto_bls] = True
ss.write('%s/%s.HH.uvfits' % (args.outdir, args.obs),
'uvfits',
UV=UV1 + UV2,
write_kwargs={
'force_phase': True,
'spoof_nonessential': True
})
outpath = '%s/test_combines' % basedir
insarray = []
for x in obs:
read_paths = util.read_paths_construct(basedir, 'None', x, 'INS')
insarray.append(
INS(obs=x,
outpath=outpath,
read_paths=read_paths,
flag_choice='orginal'))
inscombined = INS_helpers.INS_concat(insarray, axis=0)
inscombined.obs = 'LWA_50to60'
inscombined.outpath = outpath
inscombined.vis_units = insarray[0].vis_units
inscombined.pols = insarray[0].pols
inscombined.save()
cp.INS_plot(inscombined, vmax=.05, ms_vmax=5, ms_vmin=-5)
shape_dict = {'44MHZ': [44e6, 45e6], '40.6MHZ': [40.4e6, 40.7e6]}
sig_thresh = 4.35
mf = MF(inscombined,
shape_dict=shape_dict,
sig_thresh=sig_thresh,
point=False,
streak=False)
mf.apply_match_test()
cp.MF_plot(mf, ms_vmin=-mf.sig_thresh, ms_vmax=mf.sig_thresh)
from SSINS import SS
from SSINS import Catalog_Plot as cp
inpath = '/Users/mike_e_dubs/MWA/Data/1061313128_noflag.uvfits'
outpath = '/Users/mike_e_dubs/MWA/1061313128_noflag'
obs = '1061313128'
ss = SS(inpath=inpath,
outpath=outpath,
obs=obs,
bad_time_indices=[0, -1, -2, -3, -4],
read_kwargs={
'ant_str': 'cross',
'file_type': 'uvfits'
})
ss.MF_prepare(sig_thresh=5,
shape_dict={
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8],
'TV7_Broad': [1.79e8, 1.9e8]
})
cp.INS_plot(ss.INS, ms_vmin=-5, ms_vmax=5)
ss.MF.apply_match_test()
cp.MF_plot(ss.MF, ms_vmin=-5, ms_vmax=5)
shape_dict = {label: [min, max] for (label, min, max) in zip(args.labels, args.mins, args.maxs)}
else:
shape_dict = {}
obslist = util.make_obslist(args.obsfile)
edges = [0 + 16 * i for i in range(24)] + [15 + 16 * i for i in range(24)]
freqs = np.load('/Users/mike_e_dubs/python_stuff/MJW-MWA/Useful_Information/MWA_Highband_Freq_Array.npy')
for i, obs in enumerate(obslist):
read_paths = util.read_paths_INS(args.basedir, args.flag_choice, obs)
ins = INS(read_paths=read_paths, flag_choice=args.flag_choice, obs=obs,
outpath=args.outdir, order=args.order)
cp.INS_plot(ins)
mf = MF(ins, sig_thresh=5, shape_dict=shape_dict)
mf.apply_match_test(order=args.order)
cp.MF_plot(mf)
if not i:
occ_num = np.ma.masked_array(mf.INS.data.mask)
occ_den = np.ma.masked_array(np.ones(occ_num.shape))
occ_num[:, 0, edges] = np.ma.masked
occ_den[:, 0, edges] = np.ma.masked
else:
occ_num = occ_num + mf.INS.data.mask
occ_den = occ_den + np.ones(occ_num.shape)
occ_freq = occ_num.sum(axis=(0, 1, 3)) / occ_den.sum(axis=(0, 1, 3)) * 100
occ_total = occ_num.sum() / occ_den.sum() * 100
fig, ax = plt.subplots(figsize=(14, 8))
plot_lib.error_plot(fig, ax, freqs * 10**(-6), occ_freq,
title='Golden Set RFI Frequency Post COTTER',
xlabel='Frequency (Mhz)', ylabel='RFI Occupancy %%',
