def read_spd_file(spdfn):
"""Return a SPCandidate object for the spd given.
Input:
spdfn: *.spd file name.
Output:
cand: Candidate object constructed from the given pfd
file name.
"""
#spd = sp_utils.spd(spdfn)
spd = read_spd.spd(spdfn)
cand = SPCandidate(spd.best_dm, spd.ra_deg, spd.dec_deg, spdfn)
return cand
def _compute_data(self, cand):
"""Create an sp_utils.spd object for the candidate's
*.spd file
Input:
cand: A ratings2.0 SPCandidate object.
Output:
spd: The corresponding sp_utils.spd object.
"""
spdfn = cand.info['spdfn']
spd = read_spd.spd(spdfn)
return spd
def _compute_data(self, cand):
"""Create an sp_utils.spd object for the candidate's
*.spd file
Input:
cand: A ratings2.0 SPCandidate object.
Output:
spd: The corresponding sp_utils.spd object.
"""
spdfn = cand.info['spdfn']
spd = read_spd.spd(spdfn)
return spd
def plot(spdfile, singlepulsefiles=None, spec_width=1.5, loc_pulse=0.5, xwin=False, outfile="spdplot", just_waterfall=True, \
integrate_spec=True, integrate_ts=True, disp_pulse=True, tar=None):
"""
Generates spd plots which include the following subplots:
De-dispersed Zero-DM filtered Waterfall plot
De-dispersed Waterfall plot
optional subplots:
Dispersed Zero-DM filtered Waterfall plot (Inset of the corresponding dedispersed plot).
Dispersed Waterfall plot ((Inset of the corresponding dedispersed plot).).
Dedispersed zero-DM filtered time series for the corresponding waterfall plot.
Dedispersed time series for the corresponding waterfall plot.
Spectra of the de-dispersed pulse for each of the above waterfalled plots.
SNR vs DM
DM vs. Time
Inputs:
spdfile: A .spd file.
Optional Inputs:
spec_width: Twice this number times the pulse_width around the pulse to consider for the spectrum
loc_pulse: Fraction of the window length where the pulse is located.(eg. 0.25 = 1/4th of the way in.
0.5 = middle of the plot)
singlepulsefiles: list of .singlepulse files
xwin: plot in an xwin window?
outfile: name of the output file you want.
just_waterfall: Do you only want to display the waterfall plots?
integrate_spec: Do you want to show the pulse spectrum?
integrate_ts: Do you want to show the time series?
disp_pulse: Do you want to show the inset dispersed pulse?
tar: Supply the tarball of the singlepulse files instead of individual files.
"""
if not spdfile.endswith(".spd"):
raise ValueError("The first file must be a .spd file")
#npzfile = np.load(spdfile)
spdobj = read_spd.spd(spdfile)
##### Read in the header information and other required variables for the plots. ######
#text_array = npzfile['text_array']
man_params = spdobj.man_params
fn = spdobj.filename
telescope = spdobj.telescope
RA = spdobj.ra
dec = spdobj.dec
MJD = spdobj.mjd
mjd = Popen(["mjd2cal", "%f"%MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
rank = spdobj.rank
nsub = spdobj.waterfall_nsubs
nbins = spdobj.nsamp
subdm = dm = sweep_dm = spdobj.best_dm
sigma = spdobj.sigma
sample_number = spdobj.pulse_peak_sample
duration = spdobj.waterfall_duration
width_bins = spdobj.pulsewidth_bins
pulse_width = spdobj.pulsewidth_seconds
tsamp = spdobj.tsamp
Total_observed_time = spdobj.total_obs_time
topo_start = spdobj.pulse_peak_time
start = topo_start - loc_pulse*duration
datastart = spdobj.waterfall_start_time
datasamp = spdobj.waterfall_tsamp
datanumspectra = spdobj.waterfall_prededisp_nbins
min_freq = spdobj.min_freq
max_freq = spdobj.max_freq
sweep_duration = spdobj.sweep_duration
sweeped_start = spdobj.sweep_start_time
bary_start = spdobj.bary_pulse_peak_time
downsamp = datasamp/tsamp
if xwin:
pgplot_device = "/XWIN"
else:
pgplot_device = ""
if pgplot_device:
sp_pgplot.ppgplot.pgopen(pgplot_device)
else:
if (outfile == "spdplot"): # default filename
if rank:
sp_pgplot.ppgplot.pgopen(fn[:-5]+'_DM%.1f_%.1fs_rank_%i.spd.ps/VPS'%(subdm, (start+loc_pulse*duration), rank))
else:
sp_pgplot.ppgplot.pgopen(fn[:-5]+'_DM%.1f_%.1fs.spd.ps/VPS'%(subdm, (start+loc_pulse*duration)))
else:
if rank:
sp_pgplot.ppgplot.pgopen(outfile+'_DM%.1f_%.1fs_rank_%i.spd.ps/VPS'%(subdm, (start+loc_pulse*duration), rank))
else:
sp_pgplot.ppgplot.pgopen(outfile+'_DM%.1f_%.1fs.spd.ps/VPS'%(subdm, (start+loc_pulse*duration)))
if (just_waterfall == False):
sp_pgplot.ppgplot.pgpap(10.25, 8.5/11.0)
# Dedispersed waterfall plot - zerodm - OFF
array = spdobj.data_nozerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.50, 0.80)
sp_pgplot.ppgplot.pgswin(datastart-start, datastart-start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
if not integrate_spec:
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp], rangey = [min_freq, max_freq], image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - Off
Dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.80, 0.90)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+duration, np.min(Dedisp_ts), 1.05*np.max(Dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,Dedisp_ts)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgsci(1)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(Dedisp_ts)])
erry2 = np.array([np.std(Dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - Off
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.5, 0.8)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
#Dedispersed waterfall plot - Zerodm ON
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.1, 0.40)
sp_pgplot.ppgplot.pgswin(datastart-start , datastart-start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time - %.2f s"%datastart)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
if not integrate_spec:
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
array = spdobj.data_zerodm_dedisp.astype(np.float64)
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp],rangey = [min_freq, max_freq],image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - On
dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.40, 0.50)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+duration, np.min(dedisp_ts), 1.05*np.max(dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,dedisp_ts)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(dedisp_ts)])
erry2 = np.array([np.std(dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - On
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.1, 0.4)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
if disp_pulse:
# Sweeped waterfall plot Zerodm - OFF
array = spdobj.data_nozerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.50, 0.70)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
delays = spdobj.dmsweep_delays
freqs = spdobj.dmsweep_freqs
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgslw(3)
# Sweeped waterfall plot Zerodm - ON
array = spdobj.data_zerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.1, 0.3)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
#### Figure texts
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.81, 0.97, 0.64, 0.909)
sp_pgplot.ppgplot.pgsch(0.62)
else:
sp_pgplot.ppgplot.pgsvp(0.745, 0.97, 0.64, 0.909)
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.01, 0.0, "RA: %s" %RA)
sp_pgplot.ppgplot.pgmtxt('T', -2.6, 0.01, 0.0, "DEC: %s" %dec)
sp_pgplot.ppgplot.pgmtxt('T', -4.1, 0.01, 0.0, "MJD: %f" %MJD)
sp_pgplot.ppgplot.pgmtxt('T', -5.6, 0.01, 0.0, "Obs date: %s %s %s" %(date[0], date[1], date[2]))
sp_pgplot.ppgplot.pgmtxt('T', -7.1, 0.01, 0.0, "Telescope: %s" %telescope)
sp_pgplot.ppgplot.pgmtxt('T', -8.6, 0.01, 0.0, "DM: %.2f pc cm\u-3\d" %dm)
if sigma:
sp_pgplot.ppgplot.pgmtxt('T', -10.1, 0.01, 0.0, "S/N\dMAX\u: %.2f" %sigma)
else:
sp_pgplot.ppgplot.pgmtxt('T', -10.1, 0.01, 0.0, "S/N\dMAX\u: N/A")
sp_pgplot.ppgplot.pgmtxt('T', -11.6, 0.01, 0.0, "Number of samples: %i" %nbins)
sp_pgplot.ppgplot.pgmtxt('T', -13.1, 0.01, 0.0, "Number of subbands: %i" %nsub)
sp_pgplot.ppgplot.pgmtxt('T', -14.6, 0.01, 0.0, "Pulse width: %.2f ms" %(pulse_width*1e3))
sp_pgplot.ppgplot.pgmtxt('T', -16.1, 0.01, 0.0, "Sampling time: %.3f \gms" %(tsamp*1e6))
sp_pgplot.ppgplot.pgmtxt('T', -17.6, 0.0, 0.0, "Bary pulse peak time: %.2f s" %(bary_start))
sp_pgplot.ppgplot.pgsvp(0.07, 0.7, 0.01, 0.05)
sp_pgplot.ppgplot.pgmtxt('T', -2.1, 0.01, 0.0, "%s" %fn)
#DM vs SNR
if not man_params:
dm_arr = np.float32(spdobj.dmVt_this_dms)
sigma_arr = np.float32 (spdobj.dmVt_this_sigmas)
time_arr = np.float32 (spdobj.dmVt_this_times)
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
sp_pgplot.ppgplot.pgswin(np.min(dm_arr), np.max(dm_arr), 0.95*np.min(sigma_arr), 1.05*np.max(sigma_arr))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
sp_pgplot.ppgplot.pgpt(dm_arr, sigma_arr, 20)
else:
dm_arr = np.array([])
sigma_arr = np.array([])
time_arr = np.array([])
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
# DM vs Time
print "Making arrays for DM vs time plot"
spfiles = singlepulsefiles
threshold = 5.0
if len(spfiles) > 2:
dm_list = map(np.float32, list(dm_arr))
time_list = map(np.float32, list(time_arr))
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.97, 0.1, 0.54)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.97, 0.1, 0.54)
dms, times, sigmas, widths, filelist = spio.gen_arrays(dm_arr, spfiles, tar, threshold)
sp_pgplot.dm_time_plot(dms, times, sigmas, dm_list, sigma_arr, time_list, Total_observed_time, xwin)
else:
print "You need a .singlepulse.tgz file to plot DM vs Time plot."
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.97, 0.1, 0.54)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.97, 0.1, 0.54)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\u-3\d)")
else:
#sp_pgplot.ppgplot.pgpap(10.25, 10.0/5.0)
sp_pgplot.ppgplot.pgpap(8.0, 1.5)
# Dedispersed waterfall plot - zerodm - OFF
array = spdobj.data_nozerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.44, 0.75)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart -start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp], rangey = [min_freq, max_freq], image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - Off
Dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.75, 0.83)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+duration, np.min(Dedisp_ts), 1.05*np.max(Dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,Dedisp_ts)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgsci(1)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(Dedisp_ts)])
erry2 = np.array([np.std(Dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - Off
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.7, 0.9, 0.44, 0.75)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
#Dedispersed waterfall plot - Zerodm ON
array = spdobj.data_zerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.05, 0.36)
sp_pgplot.ppgplot.pgswin(datastart-start , datastart-start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time - %.2f s"%datastart)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp],rangey = [min_freq, max_freq],image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - On
dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.1, 0.7, 0.36, 0.44)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+duration, np.min(dedisp_ts), 1.05*np.max(dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,dedisp_ts)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(dedisp_ts)])
erry2 = np.array([np.std(dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - On
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.70, 0.90, 0.05, 0.36)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
if disp_pulse:
# Sweeped waterfall plot Zerodm - OFF
array = spdobj.data_nozerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.44, 0.65)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
delays = spdobj.dmsweep_delays
freqs = spdobj.dmsweep_freqs
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgslw(3)
# Sweeped waterfall plot Zerodm - ON
array = spdobj.data_zerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.05, 0.25)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
#### Figure texts
sp_pgplot.ppgplot.pgsvp(0.05, 0.95, 0.8, 0.9)
sp_pgplot.ppgplot.pgsch(0.65)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.01, 0.0, "RA: %s" %RA)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.01, 0.0, "DEC: %s" %dec)
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.01, 0.0, "MJD: %f" %MJD)
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.01, 0.0, "Obs date: %s %s %s" %(date[0], date[1], date[2]))
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.35, 0.0, "Telescope: %s" %telescope)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.35, 0.0, "DM: %.2f pc cm\u-3\d" %dm)
if sigma:
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.35, 0.0, "S/N\dMAX\u: %.2f" %sigma)
else:
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.35, 0.0, "S/N\dMAX\u: N/A")
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.35, 0.0, "Number of samples: %i" %nbins)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.65, 0.0, "Number of subbands: %i" %nsub)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.65, 0.0, "Pulse width: %.2f ms" %(pulse_width*1e3))
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.65, 0.0, "Sampling time: %.3f \gms" %(tsamp*1e6))
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.65, 0.0, "Bary pulse peak time: %.2f s" %(bary_start))
sp_pgplot.ppgplot.pgiden()
sp_pgplot.ppgplot.pgclos()
def plot(spdfile, singlepulsefiles=None, spec_width=1.5, loc_pulse=0.5, xwin=False, outfile="spdplot", just_waterfall=True, \
integrate_spec=True, integrate_ts=True, disp_pulse=True, tar=None):
"""
Generates spd plots which include the following subplots:
De-dispersed Zero-DM filtered Waterfall plot
De-dispersed Waterfall plot
optional subplots:
Dispersed Zero-DM filtered Waterfall plot (Inset of the corresponding dedispersed plot).
Dispersed Waterfall plot ((Inset of the corresponding dedispersed plot).).
Dedispersed zero-DM filtered time series for the corresponding waterfall plot.
Dedispersed time series for the corresponding waterfall plot.
Spectra of the de-dispersed pulse for each of the above waterfalled plots.
SNR vs DM
DM vs. Time
Inputs:
spdfile: A .spd file.
Optional Inputs:
spec_width: Twice this number times the pulse_width around the pulse to consider for the spectrum
loc_pulse: Fraction of the window length where the pulse is located.(eg. 0.25 = 1/4th of the way in.
0.5 = middle of the plot)
singlepulsefiles: list of .singlepulse files
xwin: plot in an xwin window?
outfile: name of the output file you want.
just_waterfall: Do you only want to display the waterfall plots?
integrate_spec: Do you want to show the pulse spectrum?
integrate_ts: Do you want to show the time series?
disp_pulse: Do you want to show the inset dispersed pulse?
tar: Supply the tarball of the singlepulse files instead of individual files.
"""
if not spdfile.endswith(".spd"):
raise ValueError("The first file must be a .spd file")
#npzfile = np.load(spdfile)
spdobj = read_spd.spd(spdfile)
##### Read in the header information and other required variables for the plots. ######
#text_array = npzfile['text_array']
man_params = spdobj.man_params
fn = spdobj.filename
telescope = spdobj.telescope
RA = spdobj.ra
dec = spdobj.dec
MJD = spdobj.mjd
mjd = Popen(["mjd2cal", "%f" % MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
rank = spdobj.rank
nsub = spdobj.waterfall_nsubs
nbins = spdobj.nsamp
subdm = dm = sweep_dm = spdobj.best_dm
sigma = spdobj.sigma
sample_number = spdobj.pulse_peak_sample
duration = spdobj.waterfall_duration
width_bins = spdobj.pulsewidth_bins
pulse_width = spdobj.pulsewidth_seconds
tsamp = spdobj.tsamp
Total_observed_time = spdobj.total_obs_time
topo_start = spdobj.pulse_peak_time
start = topo_start - loc_pulse * duration
datastart = spdobj.waterfall_start_time
datasamp = spdobj.waterfall_tsamp
datanumspectra = spdobj.waterfall_prededisp_nbins
min_freq = spdobj.min_freq
max_freq = spdobj.max_freq
sweep_duration = spdobj.sweep_duration
sweeped_start = spdobj.sweep_start_time
bary_start = spdobj.bary_pulse_peak_time
downsamp = datasamp / tsamp
if xwin:
pgplot_device = "/XWIN"
else:
pgplot_device = ""
if pgplot_device:
sp_pgplot.ppgplot.pgopen(pgplot_device)
else:
if (outfile == "spdplot"): # default filename
if rank:
sp_pgplot.ppgplot.pgopen(
fn[:-5] + '_DM%.1f_%.1fs_rank_%i.spd.ps/VPS' %
(subdm, (start + loc_pulse * duration), rank))
else:
sp_pgplot.ppgplot.pgopen(fn[:-5] + '_DM%.1f_%.1fs.spd.ps/VPS' %
(subdm,
(start + loc_pulse * duration)))
else:
if rank:
sp_pgplot.ppgplot.pgopen(
outfile + '_DM%.1f_%.1fs_rank_%i.spd.ps/VPS' %
(subdm, (start + loc_pulse * duration), rank))
else:
sp_pgplot.ppgplot.pgopen(outfile + '_DM%.1f_%.1fs.spd.ps/VPS' %
(subdm,
(start + loc_pulse * duration)))
if (just_waterfall == False):
sp_pgplot.ppgplot.pgpap(10.25, 8.5 / 11.0)
# Dedispersed waterfall plot - zerodm - OFF
array = spdobj.data_nozerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.50, 0.80)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + datanumspectra * datasamp,
min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5,
"Observing Frequency (MHz)")
if not integrate_spec:
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5,
"Zero-dm filtering - Off")
sp_pgplot.plot_waterfall(array,
rangex=[
datastart - start, datastart - start +
datanumspectra * datasamp
],
rangey=[min_freq, max_freq],
image='apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - Off
Dedisp_ts = array[::-1].sum(axis=0)
times = np.arange(datanumspectra) * datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.80, 0.90)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + duration,
np.min(Dedisp_ts),
1.05 * np.max(Dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times, Dedisp_ts)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgsci(1)
errx1 = np.array([0.60 * (datastart - start + duration)])
erry1 = np.array([0.60 * np.max(Dedisp_ts)])
erry2 = np.array([np.std(Dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - Off
if integrate_spec:
spectrum_window = spec_width * pulse_width
window_width = int(spectrum_window / datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins * loc_pulse / downsamp)
on_spec = array[...,
burst_bin - window_width:burst_bin + window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.5, 0.8)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec),
1.05 * np.max(Dedisp_spec), min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec, freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5,
"Zero-dm filtering - Off")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
#Dedispersed waterfall plot - Zerodm ON
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.1, 0.40)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + datanumspectra * datasamp,
min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5,
"Time - %.2f s" % datastart)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5,
"Observing Frequency (MHz)")
if not integrate_spec:
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5,
"Zero-dm filtering - On")
array = spdobj.data_zerodm_dedisp.astype(np.float64)
sp_pgplot.plot_waterfall(array,
rangex=[
datastart - start, datastart - start +
datanumspectra * datasamp
],
rangey=[min_freq, max_freq],
image='apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - On
dedisp_ts = array[::-1].sum(axis=0)
times = np.arange(datanumspectra) * datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.40, 0.50)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + duration,
np.min(dedisp_ts),
1.05 * np.max(dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times, dedisp_ts)
errx1 = np.array([0.60 * (datastart - start + duration)])
erry1 = np.array([0.60 * np.max(dedisp_ts)])
erry2 = np.array([np.std(dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - On
if integrate_spec:
spectrum_window = spec_width * pulse_width
window_width = int(spectrum_window / datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins * loc_pulse / downsamp)
on_spec = array[...,
burst_bin - window_width:burst_bin + window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.1, 0.4)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec),
1.05 * np.max(Dedisp_spec), min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec, freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5,
"Zero-dm filtering - On")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
if disp_pulse:
# Sweeped waterfall plot Zerodm - OFF
array = spdobj.data_nozerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.50, 0.70)
sp_pgplot.ppgplot.pgswin(sweeped_start,
sweeped_start + sweep_duration, min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(
array,
rangex=[sweeped_start, sweeped_start + sweep_duration],
rangey=[min_freq, max_freq],
image='apjgrey')
delays = spdobj.dmsweep_delays
freqs = spdobj.dmsweep_freqs
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start - 0.2 * sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays + sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgslw(3)
# Sweeped waterfall plot Zerodm - ON
array = spdobj.data_zerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.1, 0.3)
sp_pgplot.ppgplot.pgswin(sweeped_start,
sweeped_start + sweep_duration, min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(
array,
rangex=[sweeped_start, sweeped_start + sweep_duration],
rangey=[min_freq, max_freq],
image='apjgrey')
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start - 0.2 * sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays + sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
#### Figure texts
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.81, 0.97, 0.64, 0.909)
sp_pgplot.ppgplot.pgsch(0.62)
else:
sp_pgplot.ppgplot.pgsvp(0.745, 0.97, 0.64, 0.909)
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.01, 0.0, "RA: %s" % RA)
sp_pgplot.ppgplot.pgmtxt('T', -2.6, 0.01, 0.0, "DEC: %s" % dec)
sp_pgplot.ppgplot.pgmtxt('T', -4.1, 0.01, 0.0, "MJD: %f" % MJD)
sp_pgplot.ppgplot.pgmtxt(
'T', -5.6, 0.01, 0.0,
"Obs date: %s %s %s" % (date[0], date[1], date[2]))
sp_pgplot.ppgplot.pgmtxt('T', -7.1, 0.01, 0.0,
"Telescope: %s" % telescope)
sp_pgplot.ppgplot.pgmtxt('T', -8.6, 0.01, 0.0,
"DM: %.2f pc cm\u-3\d" % dm)
if sigma:
sp_pgplot.ppgplot.pgmtxt('T', -10.1, 0.01, 0.0,
"S/N\dMAX\u: %.2f" % sigma)
else:
sp_pgplot.ppgplot.pgmtxt('T', -10.1, 0.01, 0.0, "S/N\dMAX\u: N/A")
sp_pgplot.ppgplot.pgmtxt('T', -11.6, 0.01, 0.0,
"Number of samples: %i" % nbins)
sp_pgplot.ppgplot.pgmtxt('T', -13.1, 0.01, 0.0,
"Number of subbands: %i" % nsub)
sp_pgplot.ppgplot.pgmtxt('T', -14.6, 0.01, 0.0,
"Pulse width: %.2f ms" % (pulse_width * 1e3))
sp_pgplot.ppgplot.pgmtxt('T', -16.1, 0.01, 0.0,
"Sampling time: %.3f \gms" % (tsamp * 1e6))
sp_pgplot.ppgplot.pgmtxt('T', -17.6, 0.0, 0.0,
"Bary pulse peak time: %.2f s" % (bary_start))
sp_pgplot.ppgplot.pgsvp(0.07, 0.7, 0.01, 0.05)
sp_pgplot.ppgplot.pgmtxt('T', -2.1, 0.01, 0.0, "%s" % fn)
#DM vs SNR
if not man_params:
dm_arr = np.float32(spdobj.dmVt_this_dms)
sigma_arr = np.float32(spdobj.dmVt_this_sigmas)
time_arr = np.float32(spdobj.dmVt_this_times)
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
sp_pgplot.ppgplot.pgswin(np.min(dm_arr), np.max(dm_arr),
0.95 * np.min(sigma_arr),
1.05 * np.max(sigma_arr))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
sp_pgplot.ppgplot.pgpt(dm_arr, sigma_arr, 20)
else:
dm_arr = np.array([])
sigma_arr = np.array([])
time_arr = np.array([])
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
# DM vs Time
print "Making arrays for DM vs time plot"
spfiles = singlepulsefiles
threshold = 5.0
if len(spfiles) > 2:
dm_list = map(np.float32, list(dm_arr))
time_list = map(np.float32, list(time_arr))
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.97, 0.1, 0.54)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.97, 0.1, 0.54)
dms, times, sigmas, widths, filelist = spio.gen_arrays(
dm_arr, spfiles, tar, threshold)
sp_pgplot.dm_time_plot(dms, times, sigmas, dm_list, sigma_arr,
time_list, Total_observed_time, xwin)
else:
print "You need a .singlepulse.tgz file to plot DM vs Time plot."
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.97, 0.1, 0.54)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.97, 0.1, 0.54)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\u-3\d)")
else:
#sp_pgplot.ppgplot.pgpap(10.25, 10.0/5.0)
sp_pgplot.ppgplot.pgpap(8.0, 1.5)
# Dedispersed waterfall plot - zerodm - OFF
array = spdobj.data_nozerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.44, 0.75)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + datanumspectra * datasamp,
min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5,
"Observing Frequency (MHz)")
sp_pgplot.plot_waterfall(array,
rangex=[
datastart - start, datastart - start +
datanumspectra * datasamp
],
rangey=[min_freq, max_freq],
image='apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - Off
Dedisp_ts = array[::-1].sum(axis=0)
times = np.arange(datanumspectra) * datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.75, 0.83)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + duration,
np.min(Dedisp_ts),
1.05 * np.max(Dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times, Dedisp_ts)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgsci(1)
errx1 = np.array([0.60 * (datastart - start + duration)])
erry1 = np.array([0.60 * np.max(Dedisp_ts)])
erry2 = np.array([np.std(Dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - Off
if integrate_spec:
spectrum_window = spec_width * pulse_width
window_width = int(spectrum_window / datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins * loc_pulse / downsamp)
on_spec = array[...,
burst_bin - window_width:burst_bin + window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.7, 0.9, 0.44, 0.75)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec),
1.05 * np.max(Dedisp_spec), min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec, freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5,
"Zero-dm filtering - Off")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
#Dedispersed waterfall plot - Zerodm ON
array = spdobj.data_zerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.05, 0.36)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + datanumspectra * datasamp,
min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5,
"Time - %.2f s" % datastart)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5,
"Observing Frequency (MHz)")
sp_pgplot.plot_waterfall(array,
rangex=[
datastart - start, datastart - start +
datanumspectra * datasamp
],
rangey=[min_freq, max_freq],
image='apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - On
dedisp_ts = array[::-1].sum(axis=0)
times = np.arange(datanumspectra) * datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.1, 0.7, 0.36, 0.44)
sp_pgplot.ppgplot.pgswin(datastart - start,
datastart - start + duration,
np.min(dedisp_ts),
1.05 * np.max(dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times, dedisp_ts)
errx1 = np.array([0.60 * (datastart - start + duration)])
erry1 = np.array([0.60 * np.max(dedisp_ts)])
erry2 = np.array([np.std(dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - On
if integrate_spec:
spectrum_window = spec_width * pulse_width
window_width = int(spectrum_window / datasamp)
#burst_bin = int(datanumspectra*loc_pulse/downsamp)
burst_bin = int(nbins * loc_pulse / downsamp)
on_spec = array[...,
burst_bin - window_width:burst_bin + window_width]
Dedisp_spec = on_spec.sum(axis=1)
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.70, 0.90, 0.05, 0.36)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec),
1.05 * np.max(Dedisp_spec), min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec, freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5,
"Zero-dm filtering - On")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
if disp_pulse:
# Sweeped waterfall plot Zerodm - OFF
array = spdobj.data_nozerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.44, 0.65)
sp_pgplot.ppgplot.pgswin(sweeped_start,
sweeped_start + sweep_duration, min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(
array,
rangex=[sweeped_start, sweeped_start + sweep_duration],
rangey=[min_freq, max_freq],
image='apjgrey')
delays = spdobj.dmsweep_delays
freqs = spdobj.dmsweep_freqs
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start - 0.2 * sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays + sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgslw(3)
# Sweeped waterfall plot Zerodm - ON
array = spdobj.data_zerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.05, 0.25)
sp_pgplot.ppgplot.pgswin(sweeped_start,
sweeped_start + sweep_duration, min_freq,
max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(
array,
rangex=[sweeped_start, sweeped_start + sweep_duration],
rangey=[min_freq, max_freq],
image='apjgrey')
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start - 0.2 * sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays + sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
#### Figure texts
sp_pgplot.ppgplot.pgsvp(0.05, 0.95, 0.8, 0.9)
sp_pgplot.ppgplot.pgsch(0.65)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.01, 0.0, "RA: %s" % RA)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.01, 0.0, "DEC: %s" % dec)
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.01, 0.0, "MJD: %f" % MJD)
sp_pgplot.ppgplot.pgmtxt(
'T', -5.3, 0.01, 0.0,
"Obs date: %s %s %s" % (date[0], date[1], date[2]))
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.35, 0.0,
"Telescope: %s" % telescope)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.35, 0.0,
"DM: %.2f pc cm\u-3\d" % dm)
if sigma:
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.35, 0.0,
"S/N\dMAX\u: %.2f" % sigma)
else:
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.35, 0.0, "S/N\dMAX\u: N/A")
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.35, 0.0,
"Number of samples: %i" % nbins)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.65, 0.0,
"Number of subbands: %i" % nsub)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.65, 0.0,
"Pulse width: %.2f ms" % (pulse_width * 1e3))
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.65, 0.0,
"Sampling time: %.3f \gms" % (tsamp * 1e6))
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.65, 0.0,
"Bary pulse peak time: %.2f s" % (bary_start))
sp_pgplot.ppgplot.pgiden()
sp_pgplot.ppgplot.pgclos()
def GBNCC_wrapper(txtfile, maskfile, fitsfilenm, path_sp_files):
"""
The pipeline should pass job.fits_filenm as argument.
"""
files = spio.get_textfile(txtfile)
min_rank = 3
groups = [i for i in range(7) if (i >= min_rank)][::-1]
numcands = 0 # counter for max number of candidates
loop_must_break = False # dont break the loop unless num of cands >100.
values = []
lis = []
ranks = []
for group in groups:
rank = group + 1
if files[group] != "Number of rank %i groups: 0 " % rank:
add_values = spio.split_parameters(rank, txtfile)
values.extend(add_values)
lis = np.append(
lis,
np.where(files == '\tRank: %i.000000' % rank)[0])
add_ranks = np.ones(len(add_values)) * rank
ranks = np.append(ranks, add_ranks)
if len(values) > 100:
values = values[0:100]
lis = lis[0:100]
#Sort candidates based on DM
zip_list = zip(values, lis, ranks)
zip_list = sorted(zip_list, key=itemgetter(0, 0))
values = [x[0] for x in zip_list]
lis = [x[1] for x in zip_list]
ranks = [x[2] for x in zip_list]
basename = fitsfilenm[:-5]
#generate subbanded file at a DM of 0 to extract observation parameters
cmd = "psrfits_subband -dm 0.0 -nsub 128 -o %s_subband_0.0 %s" % (
basename, fitsfilenm)
print "executing %s" % cmd
subprocess.call(cmd, shell=True)
subfilenm = basename + "_subband_0.0_0001.fits"
subfile = psrfits.PsrfitsFile(subfilenm)
for ii in range(len(values)):
dm_list, time_list, dm_arr, sigma_arr, width_arr = spio.read_RRATrap_info(
txtfile, lis[ii], int(ranks[ii]))
wrapper_cand = spcand.params()
wrapper_cand.read_from_file(values[ii], subfile.tsamp, subfile.specinfo.N, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
subfile, loc_pulse=0.5, dedisp = True,\
scaleindep = None, zerodm = None, mask = None,\
barytime=True, nsub = None, bandpass_corr = False)
temp_filename = basename + "_rank_%i" % int(ranks[ii])
if ii == 0: #check if index is 0
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = 0, prevsubfile = '', \
init_flag = 1)
else:
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = prevDM, \
prevsubfile=prevsubfile, init_flag = 0)
path = os.path.dirname(os.path.abspath(__file__))
cmd = "python " + path + "/make_spd.py --use_manual_params --subdm %f --nsub %d" \
" --dm %f -T %f -t %f --width-bins %d --downsamp %d --show-spec"\
" --noplot --notopo -o %s %s " %(wrapper_cand.subdm, \
wrapper_cand.nsub, wrapper_cand.dm, \
wrapper_cand.topo_start_time, wrapper_cand.duration, \
wrapper_cand.width_bins, wrapper_cand.downsamp,\
temp_filename, correct_rawdatafile)
print "executing %s" % cmd
subprocess.call(cmd, shell=True)
# Add additional information to the header information array
text_array = np.array([correct_rawdatafile, subfile.specinfo.telescope, \
subfile.specinfo.ra_str, subfile.specinfo.dec_str, \
subfile.specinfo.start_MJD[0], int(ranks[ii]), \
wrapper_cand.nsub, wrapper_cand.nbins, \
wrapper_cand.subdm, wrapper_cand.sigma, wrapper_cand.sample_number, \
wrapper_cand.duration, wrapper_cand.width_bins, wrapper_cand.pulse_width, \
subfile.tsamp, subfile.specinfo.T, wrapper_cand.topo_start_time])
temp_filename += "_DM%.1f_%.1fs" % (wrapper_cand.subdm,
wrapper_cand.topo_start_time)
spd = read_spd.spd(temp_filename + '.spd')
spd.man_params = None
text_array = np.append(text_array, spd.waterfall_start_time)
text_array = np.append(text_array, spd.waterfall_tsamp)
text_array = np.append(text_array, spd.waterfall_prededisp_nbins)
text_array = np.append(text_array, spd.min_freq)
text_array = np.append(text_array, spd.max_freq)
text_array = np.append(text_array, spd.sweep_duration)
text_array = np.append(text_array, spd.sweep_start_time)
text_array = np.append(text_array, spd.bary_pulse_peak_time)
text_array = np.append(text_array, spd.man_params)
with open(temp_filename + ".spd", 'wb') as f:
np.savez_compressed(f, \
Data_dedisp_nozerodm = spd.data_nozerodm_dedisp,\
Data_dedisp_zerodm = spd.data_zerodm_dedisp,\
Data_nozerodm = spd.data_nozerodm,\
delays_nozerodm = spd.dmsweep_delays, \
freqs_nozerodm = spd.dmsweep_freqs,\
Data_zerodm = spd.data_zerodm, \
dm_arr= map(np.float16, dm_arr),\
sigma_arr = map(np.float16, sigma_arr), \
width_arr =map(np.uint8, width_arr),\
dm_list= map(np.float16, dm_list), \
time_list = map(np.float16, time_list), \
text_array = text_array)
plot_spd.plot(temp_filename+".spd", glob.glob(path_sp_files+'/*.singlepulse'), maskfile, \
outfile=basename, just_waterfall=False, \
integrate_spec=True, integrate_ts=True, \
disp_pulse=False, bandpass_corr = True, tar = None)
numcands += 1
print 'Finished sp_candidate : %i' % numcands
if numcands >= 100: # Max number of candidates to plot 100.
loop_must_break = True
break
def get_spcandidates(versionnum, directory, header_id=None, timestamp_mjd=None, inst_cache=None):
"""Return single pulse candidates to common DB.
Inputs:
versionnum: A combination of the githash values from
PRESTO, the pipeline, and psrfits_utils.
directory: The directory containing results from the pipeline.
header_id: header_id number for this beam, as returned by
spHeaderLoader/header.upload_header
timestamp_mjd: mjd timestamp for this observation (default=None).
inst_cache: ratings2 RatingInstanceIDCache instance.
Ouputs:
sp_cands: List of single pulse candidates, plots and tarballs.
tempdir: Path of temporary directory that SPDs have been untarred,
returned so that it can be deleted after successful SPD upload.
"""
sp_cands = []
# Create temporary directory
N = 6
prefix = "/localscratch/PALFA_spds_"
suffix = "_tmp/"
String = ''.join(random.choice(string.ascii_uppercase + string.digits + string.ascii_lowercase) for _ in range(N))
tempdir = prefix+String+suffix
os.makedirs(tempdir)
#tempdir = tempfile.mkdtemp(suffix="_tmp", prefix="PALFA_spds_")
mjd = int(timestamp_mjd)
remote_spd_base = os.path.join(config.upload.spd_ftp_dir,str(mjd))
# extract spd tarball
spd_tarfns = glob.glob(os.path.join(directory, "*_spd.tgz"))
spd_tarball = SPDTarball(spd_tarfns[0],remote_spd_base,tempdir)
spd_tempdir, spd_list = spd_tarball.extract()
remote_spd_dir = os.path.join(remote_spd_base,\
os.path.basename(spd_tarfns[0]).rstrip('_spd.tgz'))
# extract ratings tarball
rating_tarfn = spd_tarfns[0].replace("_spd","_spd_rat")
tar = tarfile.open(rating_tarfn)
try:
tar.extractall(path=tempdir)
except IOError:
if os.path.isdir(tempdir):
shutil.rmtree(tempdir)
raise SinglePulseCandidateError("Error while extracting pfd files " \
"from tarball (%s)!" % tarfn)
finally:
tar.close()
# Gather SP candidates and their rating and plots
sp_cands.append(spd_tarball)
for ii,spd_elem in enumerate(spd_list):
spdfn, spd_size = spd_elem[0], spd_elem[1]
pngfn = os.path.join(directory, \
os.path.basename(spdfn.replace(".spd",".spd.png")))
ratfn = os.path.join(tempdir, \
os.path.basename(spdfn.replace(".spd",".spd.rat")))
spd = read_spd.spd(str(spdfn))
cand = SinglePulseCandidate(ii+1, spd, versionnum, header_id=header_id)
cand.add_dependent(SinglePulseCandidatePNG(pngfn))
cand.add_dependent(SinglePulseCandidateSPD(spdfn, spd_size, remote_spd_dir=remote_spd_dir))
ratvals = ratings2.rating_value.read_file(ratfn)
cand.add_dependent(SinglePulseCandidateRating(ratvals,inst_cache=inst_cache))
sp_cands.append(cand)
# Gather per-beam plots to upload
fns = glob.glob(os.path.join(directory, "*DMs0-110_singlepulse.png"))
if len(fns) != 1:
raise SinglePulseCandidateError("Wrong number of *DMs0-110_singlepulse.png " \
"plots found (%d)!" % len(fns))
sp_cands.append(SinglePulseBeamPlotDMs0_110(fns[0], versionnum, \
header_id=header_id))
fns = glob.glob(os.path.join(directory, "*DMs100-310_singlepulse.png"))
if len(fns) != 1:
raise SinglePulseCandidateError("Wrong number of *DMs100-310_singlepulse.png " \
"plots found (%d)!" % len(fns))
sp_cands.append(SinglePulseBeamPlotDMs100_310(fns[0], versionnum, \
header_id=header_id))
fns = glob.glob(os.path.join(directory, "*DMs300-2000_singlepulse.png"))
if len(fns) != 1:
raise SinglePulseCandidateError("Wrong number of *DMs300-2000_singlepulse.png " \
"plots found (%d)!" % len(fns))
sp_cands.append(SinglePulseBeamPlotDMs300_2000(fns[0], versionnum, \
header_id=header_id))
fns = glob.glob(os.path.join(directory, "*DMs1000-10000_singlepulse.png"))
if len(fns) != 1:
raise SinglePulseCandidateError("Wrong number of *DMs1000-10000_singlepulse.png " \
"plots found (%d)!" % len(fns))
sp_cands.append(SinglePulseBeamPlotDMs1000_10000(fns[0], versionnum, \
header_id=header_id))
# Gather per-beam SP tarballs to upload
fns = glob.glob(os.path.join(directory, "*_inf.tgz"))
if len(fns) != 1:
raise SinglePulseCandidateError("Wrong number of *_inf.tgz " \
"tarballs found (%d)!" % len(fns))
sp_cands.append(SinglePulseInfTarball(fns[0], versionnum, \
header_id=header_id, timestamp_mjd=timestamp_mjd))
fns = glob.glob(os.path.join(directory, "*_singlepulse.tgz"))
if len(fns) != 1:
raise SinglePulseCandidateError("Wrong number of *_singlepulse.tgz " \
"tarballs found (%d)!" % len(fns))
sp_cands.append(SinglePulseCandsTarball(fns[0] , versionnum, \
header_id=header_id, timestamp_mjd=timestamp_mjd))
return sp_cands, tempdir
def GBNCC_wrapper(txtfile, maskfile, fitsfilenm, path_sp_files):
"""
The pipeline should pass job.fits_filenm as argument.
"""
files = spio.get_textfile(txtfile)
min_rank = 3
groups = [i for i in range(7) if(i>=min_rank)][::-1]
numcands=0 # counter for max number of candidates
loop_must_break = False # dont break the loop unless num of cands >100.
values = []
lis = []
ranks = []
for group in groups:
rank = group+1
if files[group] != "Number of rank %i groups: 0 "%rank:
add_values = spio.split_parameters(rank, txtfile)
values.extend(add_values)
lis = np.append(lis, np.where(files == '\tRank: %i.000000'%rank)[0])
add_ranks = np.ones(len(add_values)) * rank
ranks = np.append(ranks, add_ranks)
if len(values) > 100:
values = values[0:100]
lis = lis[0:100]
#Sort candidates based on DM
zip_list = zip(values, lis, ranks)
zip_list = sorted(zip_list, key=itemgetter(0,0))
values = [x[0] for x in zip_list]
lis = [x[1] for x in zip_list]
ranks = [x[2] for x in zip_list]
basename = fitsfilenm[:-5]
#generate subbanded file at a DM of 0 to extract observation parameters
cmd = "psrfits_subband -dm 0.0 -nsub 128 -o %s_subband_0.0 %s"%(basename,fitsfilenm)
print "executing %s" %cmd
subprocess.call(cmd, shell=True)
subfilenm = basename + "_subband_0.0_0001.fits"
subfile = psrfits.PsrfitsFile(subfilenm)
for ii in range(len(values)):
dm_list, time_list, dm_arr, sigma_arr, width_arr = spio.read_RRATrap_info(txtfile, lis[ii], int(ranks[ii]))
wrapper_cand = spcand.params()
wrapper_cand.read_from_file(values[ii], subfile.tsamp, subfile.specinfo.N, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
subfile, loc_pulse=0.5, dedisp = True,\
scaleindep = None, zerodm = None, mask = None,\
barytime=True, nsub = None, bandpass_corr = False)
temp_filename = basename+"_rank_%i"%int(ranks[ii])
if ii == 0: #check if index is 0
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = 0, prevsubfile = '', \
init_flag = 1)
else:
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = prevDM, \
prevsubfile=prevsubfile, init_flag = 0)
path = os.path.dirname(os.path.abspath(__file__))
cmd = "python " + path + "/make_spd.py --use_manual_params --subdm %f --nsub %d" \
" --dm %f -T %f -t %f --width-bins %d --downsamp %d --show-spec"\
" --noplot --notopo -o %s %s " %(wrapper_cand.subdm, \
wrapper_cand.nsub, wrapper_cand.dm, \
wrapper_cand.topo_start_time, wrapper_cand.duration, \
wrapper_cand.width_bins, wrapper_cand.downsamp,\
temp_filename, correct_rawdatafile)
print "executing %s" %cmd
subprocess.call(cmd, shell=True)
# Add additional information to the header information array
text_array = np.array([correct_rawdatafile, subfile.specinfo.telescope, \
subfile.specinfo.ra_str, subfile.specinfo.dec_str, \
subfile.specinfo.start_MJD[0], int(ranks[ii]), \
wrapper_cand.nsub, wrapper_cand.nbins, \
wrapper_cand.subdm, wrapper_cand.sigma, wrapper_cand.sample_number, \
wrapper_cand.duration, wrapper_cand.width_bins, wrapper_cand.pulse_width, \
subfile.tsamp, subfile.specinfo.T, wrapper_cand.topo_start_time])
temp_filename +="_DM%.1f_%.1fs"%(wrapper_cand.subdm, wrapper_cand.topo_start_time)
spd = read_spd.spd(temp_filename+'.spd')
spd.man_params = None
text_array = np.append(text_array, spd.waterfall_start_time)
text_array = np.append(text_array, spd.waterfall_tsamp)
text_array = np.append(text_array, spd.waterfall_prededisp_nbins)
text_array = np.append(text_array, spd.min_freq)
text_array = np.append(text_array, spd.max_freq)
text_array = np.append(text_array, spd.sweep_duration)
text_array = np.append(text_array, spd.sweep_start_time)
text_array = np.append(text_array, spd.bary_pulse_peak_time)
text_array = np.append(text_array, spd.man_params)
with open(temp_filename+".spd", 'wb') as f:
np.savez_compressed(f, \
Data_dedisp_nozerodm = spd.data_nozerodm_dedisp,\
Data_dedisp_zerodm = spd.data_zerodm_dedisp,\
Data_nozerodm = spd.data_nozerodm,\
delays_nozerodm = spd.dmsweep_delays, \
freqs_nozerodm = spd.dmsweep_freqs,\
Data_zerodm = spd.data_zerodm, \
dm_arr= map(np.float16, dm_arr),\
sigma_arr = map(np.float16, sigma_arr), \
width_arr =map(np.uint8, width_arr),\
dm_list= map(np.float16, dm_list), \
time_list = map(np.float16, time_list), \
text_array = text_array)
plot_spd.plot(temp_filename+".spd", glob.glob(path_sp_files+'/*.singlepulse'), maskfile, \
outfile=basename, just_waterfall=False, \
integrate_spec=True, integrate_ts=True, \
disp_pulse=False, bandpass_corr = True, tar = None)
numcands+= 1
print 'Finished sp_candidate : %i'%numcands
if numcands >= 100: # Max number of candidates to plot 100.
loop_must_break = True
break