def waterfall_plot(data, xlims, sbs, obs_mode, sbs_interval=10, interval=10000):
num_plots = int(data.shape[1]/sbs_interval)
#print(num_plots)
f, axs = plt.subplots(num_plots, 1, figsize=(10,10), sharex=True)
x = np.linspace(xlims[0], xlims[1], data.shape[0])
c = np.arange(1, int(data.shape[1]/sbs_interval)+1) #+1 so the last few plots are not completely white
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.cool)
cmap.set_array([])
leg = LofarRaw.sb_to_f(sbs[0]+np.linspace(0, data.shape[1], data.shape[1]+1)[::sbs_interval], obs_mode)
leg = [round(x.value, 2) for x in leg]*u.MHz
data = data[:,::sbs_interval]
for i,ax in enumerate(axs):
ax.plot(x, data[:,num_plots-i], c='k')
ax.fill_between(x, data[:,num_plots-i], facecolor=cmap.to_rgba(i+1))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.axhline(y=data.min(), lw=2, clip_on=False, color=cmap.to_rgba(i+1))
ax.set_yticks([])
ax.set_ylim([data.min(),data.max()])
ax.patch.set_alpha(0)
label(ax, cmap.to_rgba(i+1), leg[num_plots-i])
date_format = mdates.DateFormatter('%H:%M:%S.%f')
axs[-1].spines['bottom'].set_visible(True)
ax.xaxis.set_major_formatter(date_format)
ax.xaxis.set_minor_locator(mdates.MicrosecondLocator(interval=interval))
ax.set_xlabel('Time', fontsize=14)
ax.tick_params(labelsize=14)
f.subplots_adjust(hspace=-.5)
f.autofmt_xdate()
def get_data_lims(sbs, obs_mode, sbs_number, trange):
"""
Return the limits of the data in usable form
sbs_number = number of subbands used
trange = the length of time.
"""
flimits = np.array([0,sbs_number])
freqlimits = LofarRaw.sb_to_f(flimits+sbs[0], obs_mode)
xlims = list(map(dt.datetime.fromisoformat, [trange.start.value, trange.end.value]))
xlims = mdates.date2num(xlims)
return freqlimits, xlims
def plot_event_1d(data, xlims, sbs, obs_mode, sbs_interval=10, interval=10000):
"""
This just shows a couple 1d plots of an event up close acrdeductionoss sbs_interval frequencies (total_sbs/10)
"""
f, ax = plt.subplots(figsize=(10,6))
f.set_facecolor('white')
plt.plot(np.linspace(xlims[0], xlims[1], data.shape[0]), data[:,::sbs_interval])
plt.xlim(xlims[0], xlims[1])
plt.gca().xaxis_date()
date_format = mdates.DateFormatter('%H:%M:%S.%f')
ax.xaxis.set_major_formatter(date_format)
ax.xaxis.set_minor_locator(mdates.MicrosecondLocator(interval=interval))
f.autofmt_xdate()
plt.xlabel('Time')
plt.ylabel('Arbitrary voltages')
leg = LofarRaw.sb_to_f(sbs[0]+np.linspace(0, data.shape[1], data.shape[1]+1)[::sbs_interval], obs_mode)
leg = [round(x.value, 2) for x in leg]*u.MHz
plt.legend(leg)
plt.grid(ls=':')
plt.tight_layout()
beam1_sbs = np.arange(76, 198)
obs_mode = 3
trange = TimeRange("2020-10-13T17:45:00", 15. * u.min)
"""
fname = 'udpoutput/jupiter-stokesI_0_2020-10-13T17:47:00_19563125244140'
sbs = np.arange(76, 319)
obs_mode = 3
trange = TimeRange("2020-10-13T17:47:00", 10.*u.minute)
"""
#just plotting parameters
xlabel = "Time from 2020/10/13 17:45:00.0000"
ylabel = "Frequency (MHz)"
title = sys.argv[1].split('/')[1]
# read data
raw = LofarRaw(fname=filename, sbs=sbs, obs_mode=obs_mode, frange=frange)
# time resolution was scrubbed x16 in udpPM
flimits = np.array([40, 94])
freqlimits = raw.sb_to_f(flimits + sbs[0], obs_mode)
xlims = list(
map(dt.datetime.fromisoformat, [trange.start.value, trange.end.value]))
xlims = mdates.date2num(xlims)
print(freqlimits[0].value)
raw.data = raw.data[:, flimits[0]:flimits[1]]
### let's try remove that rfi with sk again
### do it first thing, no normalisation until afterwards
off_fname = '../udpoutput/offsource-stokesVectors_0_2020-12-15T20:04:00_19629670898060'
cal_fname = '../udpoutput/cygA-stokesVectors_0_2020-12-15T20:00:00_19629667968374' #calibrator has different obs length!
plot_names = 'test_script/Uranus_StokesI_'
plot_title = "Uranus observation - Stokes I"
frange = [15,30]
sbs = np.arange(76,198)
obs_mode = 3
time_len_mins = 176.
trange = TimeRange(filename.split('_')[-2], time_len_mins*u.min)
xlabel = "Time from {} {}".format(filename.split('_')[-2].split('T')[0], filename.split('_')[-2].split('T')[1])
xlabel = "Time on {} (UTC)".format(filename.split('_')[-2].split('T')[0])
ylabel = "Frequency (MHz)"
title = filename.split('/')[2]
no_sbs = 78 #number of usable subbands!
#how much to split up data into
nsplit = 10
r_factor = 12207
#on-beam
rawdata = LofarRaw(fname=filename, sbs=sbs, obs_mode=obs_mode, frange=frange)
rawdata.data = rawdata.data[:,:no_sbs] #need to do this because of the way subbands were set up for uranus observations! (only use 78 subbands!)
ylims, xlims = get_data_lims(no_sbs, trange)
df_chunk = data_chunker(rawdata.data, nsplit)
for i,j in enumerate(df_chunk):
data = fft_change_res()
beam1_sbs = np.arange(76, 198)
obs_mode = 3
trange = TimeRange("2020-10-13T17:45:00", 15. * u.min)
"""
fname = 'udpoutput/jupiter-stokesI_0_2020-10-13T17:47:00_19563125244140'
sbs = np.arange(76, 319)
obs_mode = 3
trange = TimeRange("2020-10-13T17:47:00", 10.*u.minute)
"""
#just plotting parameters
xlabel = "Time from 2020/10/13 17:45:00.0000"
ylabel = "Frequency (MHz)"
title = sys.argv[1].split('/')[1]
# read data
raw = LofarRaw(fname=filename, sbs=sbs, obs_mode=obs_mode, frange=frange)
# time resolution was scrubbed x16 in udpPM
flimits = np.array([40, 94])
freqlimits = raw.sb_to_f(flimits + sbs[0], obs_mode)
xlims = list(
map(dt.datetime.fromisoformat, [trange.start.value, trange.end.value]))
xlims = mdates.date2num(xlims)
print(freqlimits[0].value)
raw.data = raw.data[:, flimits[0]:flimits[1]]
#defining some of the rfi dominated bands and masking those
rfi_bands = [0, 1, 25, -1, -2, -3] #dominant rfi bands 0->20 + 31, 32, 38
rfi_freqs = raw.sb_to_f(np.array(rfi_bands) + flimits[0] + sbs[0], obs_mode)