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06_plot_rfi.py
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06_plot_rfi.py
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#!/usr/bin/env python
"""
# 06_plot_rfi.py
Plots the RFI flags
"""
import numpy as np
import seaborn as sns
import tables as tb
from leda_cal.skymodel import *
from leda_cal.leda_cal import *
from leda_cal.dpflgr import *
from leda_cal.git import get_repo_fingerprint
from scipy.stats import kurtosis, scoreatpercentile as percentile
sns.set_style('ticks')
sns.set_context("paper",font_scale=1.5)
def quicklook(filename, flatten, ant='252A'):
h5 = tb.open_file(filename)
T_ant = apply_calibration(h5)
f_leda = T_ant['f']
ant_ids = [ant,]
print("Plotting %s..." % ant_ids[0])
fig, axes = plt.subplots(figsize=(12, 6), nrows=1, ncols=1)
#plt.suptitle(h5.filename)
lst_stamps = T_ant['lst']
utc_stamps = T_ant['utc']
xlims = (f_leda[0], f_leda[-1])
#ylims = mdates.date2num((T_ant['utc'][0], T_ant['utc'][-1]))
#hfmt = mdates.DateFormatter('%m/%d %H:%M')
ylims = (T_ant['lst'][0], T_ant['lst'][-1])
T_flagged = T_ant[ant_ids[0]]
#T_flagged = np.fft.fft(T_flagged, axis=0)
#T_flagged -= T_flagged.mean(axis=0)
#T_flagged = 10*np.log10(np.abs(np.fft.ifft(T_flagged)))
T_flagged = rfi_flag(T_flagged, freqs=f_leda)
if flatten:
abp = np.ma.median(T_flagged.data, axis=0)
abp /= np.ma.median(abp)
T_flagged /= abp
clim = (percentile(T_flagged.compressed(), 5), percentile(T_flagged.compressed(), 95))
im = plt.imshow(T_flagged, # / np.median(xx, axis=0),
cmap='jet', aspect='auto',
interpolation='nearest',
clim=clim,
extent=(xlims[0], xlims[1], ylims[1], ylims[0]))
plt.title(ant_ids[0])
plt.xlabel("Frequency [MHz]")
plt.ylabel("LST [hr]")
plt.colorbar()
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.savefig("figures/rfi-flagged.pdf")
plt.show()
plt.figure()
#plt.plot(f_leda, np.sum(T_flagged.mask, axis=0).astype('float') / T_flagged.mask.shape[0], label='total')
day = T_flagged[0:2000].mask
night = T_flagged[2250:2750].mask
plt.plot(f_leda, np.sum(night, axis=0).astype('float') / night.shape[0], label='night')
plt.plot([0])
plt.plot(f_leda, np.sum(day, axis=0).astype('float') / day.shape[0], label='day')
plt.xlim(40, 85)
plt.ylim(-0.025, 0.25)
plt.title(ant_ids[0])
plt.xlabel("Frequency [MHz]")
plt.ylabel("Flagged fraction")
plt.minorticks_on()
plt.legend(frameon=True, loc=2)
plt.tight_layout()
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.savefig("figures/rfi-fraction.pdf")
plt.show()
plt.figure()
plt.plot(f_leda, kurtosis(T_flagged, axis=0))
plt.title(ant_ids[0])
plt.ylabel("Kurtosis")
plt.xlabel("Frequency [MHz]")
plt.xlim(40, 85)
plt.ylim(-50, 1600)
plt.minorticks_on()
plt.text(0.005, 0.005, get_repo_fingerprint(), transform=fig.transFigure, size=8)
plt.show()
plt.figure()
if __name__ == "__main__":
import optparse, sys
usage = '%prog [opts] filename_of_hdf5_observation'
o = optparse.OptionParser()
o.set_usage(usage)
o.set_description(__doc__)
o.add_option('--ant', dest='ant', action='store', default='252A',
help='Name of the antenna to plot. Default: 252A')
o.add_option('--flatten', dest='flatten', action='store_true', default=False,
help='Apply a crude bandpass derived from the data. Default: False')
opts, args = o.parse_args(sys.argv[1:])
if len(args) != 1:
o.print_help()
exit(1)
else:
filename = args[0]
quicklook(filename, opts.flatten, ant=opts.ant)