def test_INS_plot():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, '%s_SSINS.h5' % obs)
outdir = os.path.join(DATA_PATH, 'test_plots')
prefix = '%s/%s' % (outdir, obs)
outfile = '%s_SSINS.pdf' % prefix
log_prefix = '%s/%s_log' % (outdir, obs)
log_outfile = '%s_SSINS.pdf' % log_prefix
ins = INS(insfile)
xticks = np.arange(0, 384, 96)
xticklabels = ['%.1f' % (10**-6 * ins.freq_array[tick]) for tick in xticks]
yticks = np.arange(0, 50, 10)
yticklabels = ['%i' % (2 * tick) for tick in yticks]
cp.INS_plot(ins, prefix)
cp.INS_plot(ins,
log_prefix,
log=True,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
title='Title')
assert os.path.exists(outfile), "The first plot was not made"
assert os.path.exists(log_outfile), "The second plot was not made"
os.remove(outfile)
os.remove(log_outfile)
os.rmdir(outdir)
def test_INS_plot():
matplotlib = pytest.importorskip("matplotlib")
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
outdir = os.path.join(DATA_PATH, 'test_plots')
prefix = f'{outdir}/{obs}_raw'
outfile = f'{prefix}_SSINS.pdf'
log_prefix = f'{outdir}/{obs}_log'
log_outfile = f'{log_prefix}_SSINS.pdf'
symlog_prefix = f'{outdir}/{obs}_symlog'
symlog_outfile = f'{symlog_prefix}_SSINS.pdf'
ins = INS(insfile)
xticks = np.arange(0, 384, 96)
xticklabels = ['%.1f' % (10**-6 * ins.freq_array[tick]) for tick in xticks]
yticks = np.arange(0, 50, 10)
yticklabels = ['%i' % (2 * tick) for tick in yticks]
# Make lst straddle the 2pi boundary to trigger the warning
mean_lst = np.mean(ins.lst_array)
ins.lst_array += (2 * np.pi) - mean_lst
ins.lst_array[ins.lst_array > (2 * np.pi)] -= 2 * np.pi
with pytest.warns(UserWarning, match="LSTs appear to cross"):
cp.INS_plot(ins,
prefix,
backend='Agg',
use_extent=True,
extent_time_format='lst')
with pytest.warns(UserWarning, match="Plotting keyword"):
cp.INS_plot(ins,
log_prefix,
log=True,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
title='Title')
cp.INS_plot(ins,
symlog_prefix,
symlog=True,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
use_extent=False)
assert os.path.exists(outfile), "The first plot was not made"
assert os.path.exists(log_outfile), "The second plot was not made"
assert os.path.exists(symlog_outfile), "The third plot was not made"
os.remove(outfile)
os.remove(log_outfile)
os.remove(symlog_outfile)
os.rmdir(outdir)
def test_INS_plot():
matplotlib = pytest.importorskip("matplotlib")
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, '%s_SSINS.h5' % obs)
outdir = os.path.join(DATA_PATH, 'test_plots')
prefix = '%s/%s_raw' % (outdir, obs)
outfile = '%s_SSINS.pdf' % prefix
log_prefix = '%s/%s_log' % (outdir, obs)
log_outfile = '%s_SSINS.pdf' % log_prefix
symlog_prefix = '%s/%s_symlog' % (outdir, obs)
symlog_outfile = '%s_SSINS.pdf' % symlog_prefix
ins = INS(insfile)
xticks = np.arange(0, 384, 96)
xticklabels = ['%.1f' % (10**-6 * ins.freq_array[tick]) for tick in xticks]
yticks = np.arange(0, 50, 10)
yticklabels = ['%i' % (2 * tick) for tick in yticks]
cp.INS_plot(ins, prefix, backend='Agg')
cp.INS_plot(ins,
log_prefix,
log=True,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
title='Title')
cp.INS_plot(ins,
symlog_prefix,
symlog=True,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
use_extent=False)
assert os.path.exists(outfile), "The first plot was not made"
assert os.path.exists(log_outfile), "The second plot was not made"
assert os.path.exists(symlog_outfile), "The third plot was not made"
os.remove(outfile)
os.remove(log_outfile)
os.remove(symlog_outfile)
os.rmdir(outdir)
def test_sig_plot():
matplotlib = pytest.importorskip("matplotlib")
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, '%s_SSINS.h5' % obs)
outdir = os.path.join(DATA_PATH, 'test_plots')
prefix = '%s/%s_flagged' % (outdir, obs)
dataplotfile = '%s_SSINS.pdf' % prefix
outfile = '%s_SSINS_sig.pdf' % prefix
ins = INS(insfile)
shape_dict = {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8]
}
sig_thresh = {'TV6': 5, 'TV7': 5, 'TV8': 5, 'narrow': 5, 'streak': 5}
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict)
mf.apply_match_test(ins)
xticks = np.arange(0, 384, 96)
xticklabels = ['%.1f' % (10**-6 * ins.freq_array[tick]) for tick in xticks]
yticks = np.arange(0, 50, 10)
yticklabels = ['%i' % (2 * tick) for tick in yticks]
cp.INS_plot(ins, prefix)
assert os.path.exists(outfile), "The first plot was not made"
assert os.path.exists(dataplotfile), "The second plot was not made"
os.remove(outfile)
os.remove(dataplotfile)
os.rmdir(outdir)
# Pre-flagged and Post-flagged
cp.VDH_plot(ss,
prefix,
file_ext='pdf',
xlabel='Visibility Differences',
xscale='log',
yscale='log',
legend=True,
ylim=None,
density=False,
pre_flag=True,
post_flag=True,
pre_model=True,
post_model=True,
pre_label='Pre flagging',
post_label='Post flagging',
pre_model_label='Pre Model',
post_model_label='Post Model',
pre_model_color='green',
post_model_color='yellow',
pre_color='red',
post_color='blue',
font_size='medium')
# Create an INS object and use uvfits file
ins = INS(ss)
# Make a INS plot
# Pre-flagged and Post-flagged
cp.INS_plot(ins, prefix, file_ext='.pdf')
shape_dict = {
'TV4': [1.74e8, 1.82e8],
'TV5': [1.82e8, 1.9e8],
'TV6': [1.9e8, 1.98e8],
'dig1': [1.125e8, 1.15625e8],
'dig2': [1.375e8, 1.40625e8],
'dig3': [1.625e8, 1.65625e8],
'dig4': [1.875e8, 1.90625e8]
}
ss.INS_prepare()
ss.INS.save()
cp.INS_plot(ss.INS,
ms_vmin=-5,
ms_vmax=5,
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,
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)
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)
parser = argparse.ArgumentParser()
parser.add_argument('obs')
parser.add_argument('inpath')
parser.add_argument('outpath')
args = parser.parse_args()
ss = SS(inpath=args.inpath,
outpath=args.outpath,
obs=args.obs,
flag_choice='original',
bad_time_indices=[0, -1, -2, -3, -4, -5],
read_kwargs={'ant_str': 'cross'})
ss.INS_prepare()
ss.INS.save()
cp.INS_plot(ss.INS)
for sig_thresh in [5, 10, 20, 40, 80]:
ss.MF_prepare(sig_thresh=sig_thresh,
N_thresh=15,
shape_dict={
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8],
'broad6': [1.72e8, 1.83e8],
'broad7': [1.79e8, 1.9e8],
'broad8': [1.86e8, 1.97e8]
})
ss.MF.apply_match_test()
cp.INS_plot(ss.INS, sig_thresh=sig_thresh)
del ss.INS
del ss.MF
args = parser.parse_args()
if args.labels is not None:
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))
import argparse
from SSINS import util
from SSINS import INS
from SSINS import Catalog_Plot as cp
from matplotlib import cm
parser = argparse.ArgumentParser()
parser.add_argument('basedir')
parser.add_argument('outdir')
parser.add_argument('obsfile')
parser.add_argument('flag_choice')
parser.add_argument('order', type=int)
args = parser.parse_args()
obslist = util.make_obslist(args.obsfile)
for obs in 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,
ms_vmax=5,
ms_vmin=-5,
vmin=0,
vmax=0.13,
data_cmap=cm.viridis)
import pickle
obslist = util.make_obslist(
'/Users/mike_e_dubs/MWA/Obs_Lists/sidelobe_survey_obsIDs.txt')
indir = '/Users/mike_e_dubs/MWA/INS/Diffuse'
outpath = '%s_Filtered' % indir
sig_thresh = 5
shape_dict = {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8]
}
mf_kwargs = {'sig_thresh': sig_thresh, 'shape_dict': shape_dict}
occ_dict = {}
for obs in obslist:
if len(glob.glob('%s/figs/%s*' % (indir, obs))):
read_paths = util.read_paths_construct(indir, None, obs, 'INS')
ins = INS(obs=obs, outpath=outpath, read_paths=read_paths)
ins.data[-5:] = np.ma.masked
ins.data[0] = np.ma.masked
ins.data_ms = ins.mean_subtract()
mf = MF(ins, **mf_kwargs)
mf.apply_match_test()
cp.MF_plot(mf)
cp.INS_plot(ins, ms_vmin=-5, ms_vmax=5)
occ_dict['obs'] = (np.count_nonzero(ins.data_ms.mask[:-3]) /
ins.data[:-3].size)
del ins
del mf
pickle.dump(occ_dict, open('%s/occ_dict_only_subflags' % outpath, 'wb'))
ss.select(bls=snap_bls)
ss.select(ant_str='cross')
ins = INS(ss)
# Clear some memory
del ss
# Write the raw data and z-scores to h5 format
ins.write(prefix, sep='.', clobber=args.clobber)
ins.write(prefix, output_type='z_score', sep='.', clobber=args.clobber)
# Write out plots
cp.INS_plot(ins,
f'{prefix}_RAW',
vmin=0,
vmax=20000,
ms_vmin=-5,
ms_vmax=5)
# Flag FM radio
where_FM = np.where(
np.logical_and(ins.freq_array > 87.5e6, ins.freq_array < 108e6))
ins.metric_array[:, where_FM] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
ins.history += "Manually flagged the FM band. "
# Make a filter with specified settings
with open(args.shape_dict, 'r') as shape_file:
shape_dict = yaml.safe_load(shape_file)
sig_thresh = {shape: args.other_sig for shape in shape_dict}
return(xticks, xticklabels)
parser = argparse.ArgumentParser()
parser.add_argument('obslist')
args = parser.parse_args()
obslist = util.make_obslist(args.obslist)
obsdir = '/Volumes/Faramir/uvfits'
outdir = '/Users/mikewilensky/2016_funny_flag'
freqlist = np.arange(170, 200, 5) * 10**6
for obs in obslist:
filepath = '%s/%s_uvfits/%s.uvfits' % (obsdir, obs, obs)
prefix = '%s/%s' % (outdir, obs)
outpath = '%s_SSINS_data.h5' % prefix
if not os.path.exists(outpath):
ss = SS()
ss.read(filepath, read_data=False)
ss.read(filepath, times=np.unique(ss.time_array)[2:-3])
ins = INS(ss)
ins.write(prefix)
else:
ins = INS(outpath)
xticks, xticklabels = get_freq_ticks(ins.freq_array, freqlist)
print(ins.freq_array[1] - ins.freq_array[0])
cp.INS_plot(ins, '%s_SSINS.pdf' % prefix, xticks=xticks,
xticklabels=xticklabels)
indir = args.infile[:args.infile.rfind('/')]
if indir == args.outdir:
raise ValueError("indir and outdir are the same")
ss = SS()
ss.read(args.infile, flag_choice='original')
ins = INS(ss)
ins.metric_array[-5:] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
shape_dict = {
'TV4': [1.74e8, 1.81e8],
'TV5': [1.81e8, 1.88e8],
'TV6': [1.88e8, 1.95e8],
'broad6': [1.72e8, 1.83e8],
'broad7': [1.79e8, 1.9e8],
'broad8': [1.86e8, 1.98e8]
}
mf = MF(ins.freq_array, 5, shape_dict=shape_dict, N_samp_thresh=25)
mf.apply_match_test(ins, apply_samp_thresh=True)
cp.INS_plot(ins, '%s/%s' % (args.outdir, args.obsid))
ss.apply_flags(flag_choice='INS', INS=ins)
ss.write('%s/%s.uvfits' % (args.outdir, args.obsid),
'uvfits',
nsample_default=16)
from SSINS import Catalog_Plot as cp
from SSINS import MF
import numpy as np
import os
obslist_path = '/Users/mike_e_dubs/HERA/Interesting_Obs_Master.txt'
obslist = util.make_obslist(obslist_path)
basedirs = ['/Users/mike_e_dubs/HERA/INS/IDR2_Prelim_Nocut/HERA_IDR2_Prelim_Set_nocut',
'/Users/mike_e_dubs/HERA/INS/IDR2_OR/HERA_IDR2_Prelim_Set_OR_original']
shape_dict = {'dig1': [1.125e8, 1.15625e8],
'dig2': [1.375e8, 1.40625e8],
'dig3': [1.625e8, 1.65625e8],
'dig4': [1.875e8, 1.90625e8],
'TV4': [1.74e8, 1.82e8],
'TV5': [1.82e8, 1.9e8],
'TV6': [1.9e8, 1.98e8],
'TV7': [1.98e8, 2.06e8]}
for i, flag_choice in enumerate(['None', 'original']):
for obsid in obslist:
for pol in ['xx', 'yy', 'xy', 'yx']:
obs = 'zen.%s.%s.HH' % (obsid, pol)
read_paths = util.read_paths_INS(basedirs[i], flag_choice, obs)
ins = INS(read_paths=read_paths, obs=obs, flag_choice=flag_choice,
outpath='/Users/mike_e_dubs/HERA/INS/Adam_Presentation/%s' % flag_choice)
cp.INS_plot(ins, vmax=0.05, ms_vmax=5, ms_vmin=-5)
if flag_choice is 'None':
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)
ss = SS()
if args.correct:
ss.read(args.uvd, phase_to_pointing_center=True,
correct_cable_len=True, flag_choice='original', diff=True,
remove_dig_gains=True, remove_coarse_band=True,
data_array_dtype=np.complex64)
else:
ss.read(args.uvd, phase_to_pointing_center=True,
correct_cable_len=True, flag_choice='original', diff=True,
remove_dig_gains=False, remove_coarse_band=False,
data_array_dtype=np.complex64)
ins = INS(ss)
prefix = f'{args.outdir}/{args.obsid}'
Catalog_Plot.INS_plot(ins, prefix, data_cmap=cm.plasma, ms_vmin=-5, ms_vmax=5,
title=args.obsid, xlabel='Frequency (Mhz)',
ylabel='Time (UTC)')
# Try to save memory - hope for garbage collector
del ss
# Set up MF flagging for routine shapes
shape_dict = {'TV6': [1.74e8, 1.81e8], 'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8], 'TV9': [1.95e8, 2.02e8]}
sig_thresh = {shape: 5 for shape in shape_dict}
sig_thresh['narrow'] = 5
sig_thresh['streak'] = 10
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict,
tb_aggro=0.5, broadcast_streak=True)
# Do the flagging
def execbody(ins_filepath):
slash_ind = ins_filepath.rfind('/')
obsid = ins_filepath[slash_ind + 1:slash_ind + 11]
ins = INS(ins_filepath)
ins.select(times=ins.time_array[3:-3])
ins.metric_ms = ins.mean_subtract()
shape_dict = {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8],
'TV9': [1.95e8, 2.02e8]
}
sig_thresh = {shape: 5 for shape in shape_dict}
sig_thresh['narrow'] = 5
sig_thresh['streak'] = 8
mf = MF(ins.freq_array,
sig_thresh,
shape_dict=shape_dict,
N_samp_thresh=len(ins.time_array) // 2)
ins.metric_array[ins.metric_array == 0] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
ins.sig_array = np.ma.copy(ins.metric_ms)
#write plots if command flagged to do so
if args.plots:
prefix = '%s/%s_trimmed_zeromask' % (args.outdir, obsid)
freqs = np.arange(1.7e8, 2e8, 5e6)
xticks, xticklabels = util.make_ticks_labels(freqs,
ins.freq_array,
sig_fig=0)
yticks = [0, 20, 40]
yticklabels = []
for tick in yticks:
yticklabels.append(
Time(ins.time_array[tick], format='jd').iso[:-4])
Catalog_Plot.INS_plot(ins,
prefix,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
data_cmap=cm.plasma,
ms_vmin=-5,
ms_vmax=5,
title=obsid,
xlabel='Frequency (Mhz)',
ylabel='Time (UTC)')
if args.verbose:
print("wrote trimmed zeromask plot for " + obsid)
mf.apply_match_test(ins, apply_samp_thresh=False)
mf.apply_samp_thresh_test(ins, event_record=True)
flagged_prefix = '%s/%s_trimmed_zeromask_MF_s8' % (args.outdir, obsid)
#write data/mask/match/ if command flagged to do so
if args.write:
ins.write(flagged_prefix, output_type='data', clobber=True)
ins.write(flagged_prefix, output_type='mask', clobber=True)
ins.write(flagged_prefix, output_type='match_events')
if args.verbose:
print("wrote data/mask/match files for " + obsid)
#write plots if command flagged to do so
if args.plots:
Catalog_Plot.INS_plot(ins,
flagged_prefix,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
data_cmap=cm.plasma,
ms_vmin=-5,
ms_vmax=5,
title=obsid,
xlabel='Frequency (Mhz)',
ylabel='Time (UTC)')
if args.verbose:
print("wrote trimmed zeromask (w/ match filter) for " + obsid)
#a hardcoded csv generator for occ_csv
if args.gencsv is not None:
csv = "" + obsid + "," + flagged_prefix + "_SSINS_data.h5," + flagged_prefix + "_SSINS_mask.h5," + flagged_prefix + "_SSINS_match_events.yml\n"
with open(args.gencsv, "a") as csvfile:
csvfile.write(csv)
print("wrote entry for " + obsid)
ss.read(args.uvd, phase_to_pointing_center=True, correct_cable_len=True,
flag_choice='original', diff=True)
ins = INS(ss)
prefix = '%s/%s' % (args.outdir, args.obsid)
ins.write(prefix)
freqs = np.arange(1.7e8, 2e8, 5e6)
xticks, xticklabels = util.make_ticks_labels(freqs, ins.freq_array, sig_fig=0)
yticks = [0, 20, 40]
yticklabels = []
for tick in yticks:
yticklabels.append(Time(ins.time_array[tick], format='jd').iso[:-4])
Catalog_Plot.INS_plot(ins, prefix,
xticks=xticks, yticks=yticks, xticklabels=xticklabels,
yticklabels=yticklabels, data_cmap=cm.plasma,
ms_vmin=-5, ms_vmax=5, title=args.obsid,
xlabel='Frequency (Mhz)', ylabel='Time (UTC)')
# Try to save memory - hope for garbage collector
del ss
# Set up MF flagging for routine shapes
shape_dict = {'TV6': [1.74e8, 1.81e8], 'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8], 'TV9': [1.95e8, 2.02e8]}
sig_thresh = {shape: 5 for shape in shape_dict}
sig_thresh['narrow'] = 5
sig_thresh['streak'] = 8
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict,
N_samp_thresh=len(ins.time_array) // 2)
mf.apply_match_test(ins, apply_samp_thresh=False)
mf.apply_samp_thresh_test(ins, event_record=True)
Catalog_Plot.INS_plot(ins, '%s_flagged' % prefix,
from __future__ import absolute_import, division, print_function
import argparse
from SSINS import Catalog_Plot as cp
from SSINS import SS, INS
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('obs', action='store', help='The observation ID')
parser.add_argument('inpath', action='store', help='The path to the data file')
parser.add_argument('outpath',
action='store',
help='The base directory for saving all outputs')
args = parser.parse_args()
ss = SS()
ss.read(args.inpath, ant_str='cross')
ins = INS(ss)
prefix = '%s/%s' % (args.outpath, args.obs)
ins.write(prefix)
cp.INS_plot(ins, prefix)
