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)
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],
cmap=cm.coolwarm, title=pols[m], xlabel='Frequency (Mhz)',
ylabel='Obs', cbar_label='Fraction', xticks=xticks, xminors=xminors,
yminors=yminors, xticklabels=xticklabels, zero_mask=False,
mask_color='black')
pl.image_plot(fig_INS_s, ax_INS_s[m / 2][m % 2], INS_stack[:, 0, :, m],
title=pols[m], cbar_label='Amplitude (UNCALIB)', xticks=xticks,
aspect_ratio=0.1, xminors=xminors, xticklabels=xticklabels,
zero_mask=False)
pl.image_plot(fig_FD_s, ax_FD_s[m / 2][m % 2], FD_stack[:, 0, :, m],
cmap=cm.coolwarm, title=pols[m], cbar_label='Fraction',
aspect_ratio=0.1, xticks=xticks, xminors=xminors,
xticklabels=xticklabels, zero_mask=False, mask_color='black')
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')
if not os.path.exists('%s%i/' % (outpath, sig_thresh)):
os.makedirs('%s%i/' % (outpath, sig_thresh))
fig.savefig('%s%i/%s_match_filter_MS.png' % (outpath, sig_thresh, obs))
plt.close(fig)
occ = occ_num / occ_den * 100
occ_freq = (occ_freq_num.transpose() / occ_freq_den).transpose() * 100
np.save('%sRFI_occupancy_sigma.npy' % (outpath), occ)
test_args = {'ks_test': (MS[:, :, :, m], event),
'chisq_test': (MS[:, :, :, m], sig_thresh, event)}
stat, p, counts, exp, var, bins = getattr(rfiutil, test)(*test_args[test])
x = bins[:-1] + 0.5 * np.diff(bins)
pl.error_plot(fig, ax[m / 2][m % 2], x, counts, None, None, label='Measurements', drawstyle='steps-mid')
pl.error_plot(fig, ax[m / 2][m % 2], x, exp, None, np.sqrt(var), label='Fit', drawstyle='steps-mid',
title=pols[m], xlabel='$\sigma$', ylabel='Counts')
if p < alpha:
p_min = p
if p_min < 1:
INS[:, event[0], event[1]] = np.ma.masked
fig.savefig('%s/%s/%s_%s.png' % (outpath, obs, obs, slc))
plt.close(fig)"""
fig, ax = plt.subplots(figsize=(14, 8))
pl.image_plot(fig, ax, INS[:, 0, :], title='%s Match Filtered, %s' % (obs, test),
**image_plot_kwargs)
fig.savefig('%s/%s_INS_Filtered_%s.png' % (outpath, obs, test))
plt.close(fig)
"""for m, (event, hist) in enumerate(zip(events, hists)):
N = np.sum(hist[0])
p = scipy.stats.norm.cdf(hist[1][1:]) - scipy.stats.norm.cdf(hist[1][:-1])
fit = N * p
error = np.sqrt(N * p * (1 - p))
x = hist[1][:-1] + 0.5 * np.diff(hist[1])
title_tuple = (obs,
min(freqs[event[0], event[1]]) * 10 ** (-6),
max(freqs[event[0], event[1]]) * 10 ** (-6))
tag_tuple = (outpath,
obs, obs,
import plot_lib
from matplotlib import cm
UV = UVData()
UV.read_uvfits('/Users/mike_e_dubs/MWA/Data/uvfits/1061313128.uvfits')
ind_0 = np.where(UV.nsample_array == 0)
cond = np.logical_and(UV.nsample_array > 0, UV.flag_array).reshape(
[UV.Ntimes, UV.Nbls, UV.Nspws, UV.Nfreqs, UV.Npols]).sum(axis=1)
fig, ax, pols, xticks, xminors, yminors, xticklabels = plot_lib.four_panel_tf_setup(
UV.freq_array[0, :])
for m in range(4):
plot_lib.image_plot(fig,
ax[m / 2][m % 2],
cond[:, 0, :, m],
cmap=cm.plasma,
title=pols[m],
ylabel='Time (2s)',
cbar_label='Nbls',
xticks=xticks,
xminors=xminors,
yminors=yminors,
xticklabels=xticklabels,
mask_color='white')
fig.savefig(
'/Users/mike_e_dubs/MWA/Test_Plots/nsample_test/1061313128_nsample_where_flag.png'
)
for obs in obs_list:
arr = '%s%i_Vis_Avg_Amp_All.npy' % (arr_path, obs)
INS = np.ma.masked_array(np.load(arr))
FD_CB = np.zeros(INS.shape)
for m in range(24):
INS_CBM = np.array([np.median(INS[:, :, 16 * m:16 * (m + 1), :], axis=2) for k in range(16)]).transpose((1, 2, 0, 3))
FD_CB[:, :, 16 * m:16 * (m + 1), :] = INS[:, :, 16 * m:16 * (m + 1), :] / INS_CBM - 1
INS[:, :, centers, :] = np.ma.masked
INS[:, :, LEdges, :] = np.ma.masked
INS[:, :, REdges, :] = np.ma.masked
FD_CB = np.ma.masked_where(np.logical_and(FD_CB > 0.1, ~INS.mask), FD_CB)
if np.any(FD_CB.mask):
FD_CB.mask[:, :, centers, :] = False
FD_CB.mask[:, :, LEdges, :] = False
FD_CB.mask[:, :, REdges, :] = False
if obs == 1061312392:
FD_CB[:, :, centers, :] = np.ma.masked
FD_CB[:, :, LEdges, :] = np.ma.masked
FD_CB[:, :, REdges, :] = np.ma.masked
fig, ax, pols, xticks, xminors, yminors, xticklabels = pl.four_panel_tf_setup(freq_array)
for m in range(4):
pl.image_plot(fig, ax[m / 2][m % 2], FD_CB[:, 0, :, m], cmap=cm.coolwarm,
title=pols[m], cbar_label='Fraction', xticks=xticks,
xminors=xminors, yminors=yminors, xticklabels=xticklabels,
zero_mask=False, mask_color='black')
fig.savefig('%s%i_Narrowband_FD_CB.png' % (outpath, obs))
plt.close(fig)
xticks = [UV2.Nfreqs * k / 5 for k in range(5)]
xticks.append(UV2.Nfreqs - 1)
xticklabels = [
'%.1f' % (UV2.freq_array[0, tick] * 10**(-6)) for tick in xticks
]
xminors = AutoMinorLocator(2)
pols = ['XX', 'YY']
for m in range(2):
plot_lib.image_plot(fig1,
ax1[m],
fhd_flags[:, 0, :, m],
cmap=cm.binary,
title=pols[m],
aspect_ratio=1.6,
ylabel='Time (2s)',
cbar_label='Baselines Flagged',
xticks=xticks,
xticklabels=xticklabels,
zero_mask=False,
xminors=xminors)
plot_lib.image_plot(fig2,
ax2[m],
dirty_flags[:, 0, :, m],
cmap=cm.binary,
title=pols[m],
aspect_ratio=1.6,
ylabel='Time (2s)',
cbar_label='Baselines Flagged',
xticks=xticks,
xticklabels=xticklabels,
fig_exc, ax_exc = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_ratio, ax_ratio = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_line, ax_line = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_scatter, ax_scatter = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig_exc.suptitle('%s Deep Waterfall Excess' % (obslist[n]))
fig_ratio.suptitle('%s Deep Waterfall Ratio' % (obslist[n]))
fig_line.suptitle('%s Deep Waterfall Time Average' % (obslist[n]))
fig_scatter.suptitle('%s Mean vs. Rayleigh Fit Correlation' % (obslist[n]))
# Do the plotting...
for m in range(avg.shape[2]):
plot_lib.image_plot(fig_exc,
ax_exc[m / 2][m % 2],
excess[:, :, m],
title=pol_titles[m],
cbar_label='Amplitude (UNCALIB)',
xticks=xticks,
xticklabels=xticklabels,
xminors=xminors,
zero_mask=False,
cmap=cm.coolwarm)
plot_lib.image_plot(fig_ratio,
ax_ratio[m / 2][m % 2],
ratio[:, :, m],
title=pol_titles[m],
cbar_label='Excess/Average',
xticks=xticks,
xticklabels=xticklabels,
xminors=xminors,
zero_mask=False,
cmap=cm.coolwarm)
plot_lib.line_plot(
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)',
zero_mask=False,
mask_color='black',
title=pols[m],
xticks=xticks,
xticklabels=xticklabels,
xminors=xminors,
yminors=yminors)
fig_im.savefig('%s%s_streak_detect.png' % (outpath, obs))
#fig.savefig('%s%s_edge_detect.png' % (outpath, obs))
plt.close(fig)
plt.close(fig_im)
fig_Cal, ax_Cal = plt.subplots(nrows=2, figsize=(14, 8))
fig_UV, ax_UV = plt.subplots(nrows=2, figsize=(14, 8))
fig_Cal.suptitle('Calibration Flags %s' % (obs_str[n]))
fig_UV.suptitle('COTTER Flags %s' % (obs_str[n]))
pols = ['XX', 'YY']
for m in range(Cal.flag_array.shape[-1]):
plot_lib.image_plot(fig_Cal,
ax_Cal[m],
Cal.flag_array[:, 0, :, 0, m],
cmap=cm.binary,
title=pols[m],
aspect_ratio=0.33,
ylabel='Antenna Index',
cbar_label='True/False',
xticks=xticks,
xticklabels=xticklabels,
zero_mask=False,
xminors=xminors)
plot_lib.image_plot(fig_UV,
ax_UV[m],
H[:, :, m],
cmap=cm.binary,
title=pols[m],
aspect_ratio=0.33,
ylabel='Antenna Index',
cbar_label='True/False Average',
xticks=xticks,
outpath = '/Users/mike_e_dubs/MWA/Test_Plots/Match_Test_Samp_Thresh'
C = (4 / np.pi - 1)
pols = ['XX', 'YY', 'XY', 'YX']
for arr in arrs:
obs = arr[len(indir) + 1:len(indir) + 11]
for samp_thresh in range(10, 55, 5):
INS = np.ma.masked_array(np.load(arr))
Nbls = 8001 * np.ones(INS.shape)
MS = (INS / INS.mean(axis=0) - 1) * np.sqrt(Nbls / C)
INS, MS, events, hists = rfiutil.match_filter(INS,
MS,
Nbls,
outpath,
freq_array,
sig_thresh=4,
shape_dict=shape_dict,
samp_thresh=samp_thresh)
fig, ax = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
for i in range(4):
pl.image_plot(fig,
ax[i / 2][i % 2],
INS[:, 0, :, i],
title=pols[i],
cbar_label='Amplitude (UNCALIB)')
if not os.path.exists('%s/%i' % (outpath, samp_thresh)):
os.makedirs('%s/%i' % (outpath, samp_thresh))
fig.savefig('%s/%i/%s_INS_MF_ST%i.png' %
(outpath, samp_thresh, obs, samp_thresh))
plt.close(fig)
p_hist_sim, _ = np.histogram(p_sim, bins=p_bins)
hists = [[D_hist, D_hist_sim], [p_hist, p_hist_sim]]
bins = [D_bins, p_bins]
fig, ax = plt.subplots(figsize=(14, 8), nrows=2, ncols=2)
fig.suptitle('%i ks test, $\sigma$=%i' % (obs, sig_thresh))
kwargs = {'cmap': cm.coolwarm,
'xlabel': 'Frequency (Mhz)',
'cbar_label': '$\sigma$',
'xticks': xticks,
'xticklabels': xticklabels,
'xminors': AutoMinorLocator(4),
'mask_color': 'black'}
for i in range(4):
args = (fig, ax[i / 2][i % 2], MS[:, 0, :, i])
kwargs['title'] = pols[i]
pl.image_plot(*args, **kwargs)
fig.savefig('%s/%i_KS_%i.png' % (outpath, obs, sig_thresh))
plt.close(fig)
titles = ['KS Statistic Histogram', 'KS P-Value Histogram']
tags = ['KS_Stat', 'KS_P']
xlabels = ['$D_n$', 'p']
for hist, bin, title, tag, xlabel in zip(hists, bins, titles, tags, xlabels):
fig, ax = plt.subplots(figsize=(14, 8))
args = (fig, ax, bin, hist)
kwargs = {'xlog': True,
'ylog': True,
'xlabel': xlabel,
'title': title,
'legend': True}
pl.one_d_hist_plot(*args, labels=['Measurements', 'Simulation'], **kwargs)
fig.savefig('%s/%i_%s_loglog.png' % (outpath, obs, tag))