def INS_read_plot(inpath, outpath, obs, repo_dir):
if not os.path.exists(outpath):
os.makedirs(outpath)
fig_titles = {'All': 'All Baselines', 'Unflagged': 'Post-Flagging'}
for flag in fig_titles:
if flag in inpath:
fig_title = fig_titles[flag]
flag_slice = flag
INS = np.load(inpath)
INS_frac_diff = INS / INS.mean(axis=1) - 1
fig, ax, pols, xticks, xminors, yminors, xticklabels = \
plot_lib.four_panel_tf_setup(np.load('%sUseful_Information/MWA_Highband_Freq_Array.npy' %
(repo_dir)))
fig.suptitle('%s Mean-Subtracted Incoherent Noise Spectrum, %s' %
(obs, fig_title))
for m, pol in enumerate(pols):
plot_lib.image_plot(fig,
ax[m / 2][m % 2],
INS_frac_diff[:, 0, :, m],
cmap=cm.coolwarm,
title=pol,
cbar_label='Fraction of Mean',
xticks=xticks,
xminors=xminors,
yminors=yminors,
xticklabels=xticklabels,
zero_mask=False,
mask_color='black')
fig.savefig('%s%s_INS_frac_diff_%s.png' % (outpath, obs, flag_slice))
np.save('%s%s_INS_frac_diff_%s.npy' % (outpath, obs, flag_slice),
INS_frac_diff)
return (INS, INS_frac_diff)
for m, arr in enumerate(arr_list):
INS = np.load(arr)
INS_total[m] = INS.mean(axis=0)
frac_diff = INS / INS_total[m] - 1
frac_diff_total[m] = frac_diff.mean(axis=0)
if m == 0:
INS_stack = INS
FD_stack = frac_diff
else:
INS_stack = np.vstack((INS_stack, INS))
FD_stack = np.vstack((FD_stack, frac_diff))
INS_m = INS_total.mean(axis=0)
frac_diff_m = frac_diff_total.mean(axis=0)
fig_INS, ax_INS, pols, xticks, xminors, yminors, xticklabels = pl.four_panel_tf_setup(freq_array)
fig_fd, ax_fd = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_INS_s, ax_INS_s = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_FD_s, ax_FD_s = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_m, ax_m = plt.subplots(figsize=(14, 8), nrows=2)
fig_mp, ax_mp = plt.subplots(figsize=(14, 8), nrows=2)
avg_title = ['Golden Set INS Averaged Across Obs', 'Golden Set Frac Diff Averaged Across Obs']
ylabels = ['Amplitude (UNCALIB)', 'Fraction']
for m in range(4):
pl.image_plot(fig_INS, ax_INS[m / 2][m % 2], INS_total[:, 0, :, m],
title=pols[m], ylabel='Obs', cbar_label='Amplitude (UNCALIB)',
xticks=xticks, xminors=xminors, yminors=yminors,
xticklabels=xticklabels, zero_mask=False)
pl.image_plot(fig_fd, ax_fd[m / 2][m % 2], frac_diff_total[:, 0, :, m],
INS = np.ma.masked_array(np.load(arr))
# INS = rfiutil.narrowband_filter(INS, ch_ignore)
MS = INS / INS.mean(axis=0) - 1
if sig_thresh == 4:
Nbls = 8001 * np.ones(INS.shape)
INS, MS, _, _, _, _ = rfiutil.match_filter(INS, MS, Nbls, freq_array,
sig_thresh, shape_dict)
occ_num[sig_thresh - 4] += np.count_nonzero(INS.mask)
occ_den[sig_thresh - 4] += np.prod(INS.shape)
if np.count_nonzero(INS.mask) > 0:
occ_freq_num[sig_thresh - 4, :] += np.count_nonzero(INS.mask,
axis=(0, 1, 3))
occ_freq_den[sig_thresh - 4] += INS.shape[0] * INS.shape[3]
fig, ax, pols, xticks, xminors, yminors, xticklabels = pl.four_panel_tf_setup(
freq_array[0, :])
for m in range(4):
pl.image_plot(fig,
ax[m / 2][m % 2],
MS[:, 0, :, m],
cmap=cm.coolwarm,
title=pols[m],
xlabel='Frequency (Mhz)',
ylabel='Time Pair',
cbar_label='Fraction of Mean',
xticks=xticks,
xminors=xminors,
yminors=yminors,
xticklabels=xticklabels,
zero_mask=False,
mask_color='black')
with open('/Users/mike_e_dubs/MWA/Obs_Lists/P2_Streak.txt') as f:
obslist = f.read().split("\n")
arr_path = '/Users/mike_e_dubs/MWA/Catalogs/Wenyang_Phase2/data_eva/arrs/arrs/'
outpath = '/Users/mike_e_dubs/MWA/Test_Plots/streak_test/'
freq_arr = np.load(
'/Users/mike_e_dubs/python_stuff/MJW-MWA/Useful_Information/MWA_Highband_Freq_Array.npy'
)
for obs in obslist:
frac_diff = np.load('%s%s_Unflagged_Amp_INS_frac_diff.npym' %
(arr_path, obs))
fig, ax = plt.subplots(figsize=(14, 8))
fig_im, ax_im, pols, xticks, xminors, yminors, xticklabels = plot_lib.four_panel_tf_setup(
freq_arr)
fig_im.suptitle('%s Streak Flagging Test' % (obs))
frac_diff = rfiu.streak_detect(frac_diff)
# plot_lib.line_plot(fig, ax, [edge[:, 0, m] for m in range(edge.shape[2])],
# title='%s Streak Detection' % (obs), xlabel='Time', ylabel='Gradient',
# labels=['XX', 'YY', 'XY', 'YX'])
for m in range(frac_diff.shape[3]):
plot_lib.image_plot(fig_im,
ax_im[m / 2][m % 2],
frac_diff[:, 0, :, m],
cmap=cm.coolwarm,
xlabel='Frequency (Mhz)',
ylabel='Time Pair',
cbar_label='Amplitude (UNCALIB)',