def make_sig_plots(outdir, dlist, elist, freqs, sig_fig, obslist):
if not os.path.exists(outdir):
os.makedirs(outdir)
for dpath, epath, obs in zip(dlist, elist, obslist):
ins = INS(dpath, match_events_file=epath)
shape_sig_arr = np.ma.copy(ins.metric_ms)
tcolor_arr = np.ma.zeros(ins.metric_ms.shape)
Nevent = len(ins.match_events)
for event_ind, event in enumerate(ins.match_events):
shape_sig_arr[event[:2]] = event[-1]
tcolor_arr[event[:2]] = event_ind
tcolor_arr[tcolor_arr == 0] = np.ma.masked
fig, ax = plt.subplots(nrows=4, ncols=2, figsize=(16, 9))
xticks, xticklabels = util.make_ticks_labels(freqs, ins.freq_array, sig_fig=0)
for pol_ind in range(4):
plot_lib.image_plot(fig, ax[pol_ind][0], shape_sig_arr[:, :, pol_ind],
ylabel='Time (2 s)', xlabel='Frequency (Mhz)',
xticks=xticks, xticklabels=xticklabels,
cbar_label='Deviation ($\hat{\sigma}$)',
vmin=vmin, vmax=vmax, cmap=cm.coolwarm, symlog=True,
title=pol_dict[ins.polarization_array[pol_ind]])
plot_lib.image_plot(fig, ax[pol_ind][1], tcolor_arr[:, :, pol_ind],
ylabel='Time (2 s)', xlabel='Frequency (Mhz)',
xticks=xticks, xticklabels=xticklabels,
cbar_label='Flagging Iteration',
cmap=cm.viridis_r, mask_color='white',
title=pol_dict[ins.polarization_array[pol_ind]])
fig.savefig('%s/%s_flag_metaplot.pdf' % (outdir, obs))
plt.close(fig)
def test_make_ticks():
# Make up a frequency array and some frequencies to tick
freq_array = np.arange(1e8, 1.1e8, 1e6)
freqs = np.arange(1e8, 1.1e8, 2e6)
ticks, labels = util.make_ticks_labels(freqs, freq_array, sig_fig=2)
test_ticks = (np.arange(len(freq_array))[::2]).tolist()
test_labels = ['100.00', '102.00', '104.00', '106.00', '108.00']
assert np.all(ticks == test_ticks), "The ticks are not equal"
assert np.all(labels == test_labels), "The labels are not equal"
raise ValueError("indir and outdir are the same")
if args.rfi_flag:
if args.insfile is not None:
ins = INS(args.insfile, mask_file=args.maskfile)
else:
ss = SS()
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}
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)