ppgplot.pgsvp(0.1, 0.7, 0.3, 0.9)
ppgplot.pgswin(-margins, margins, -margins, margins)
zRows, zCols = numpy.shape(zoomImageData)
preview = ultracamutils.percentiles(zoomImageData, 20, 99)
ppgplot.pggray(preview, 0, zCols-1, 0, zRows-1, 0, 255, watchView['pgPlotTransform'])
# Graph of the x-direction
ppgplot.pgsvp(0.1, 0.7, 0.1, 0.2)
yMax = numpy.max(xCollapsed) * 1.1
yMin = numpy.min(xCollapsed) * 0.9
ppgplot.pgswin(-margins, margins, yMin, yMax)
ppgplot.pgsci(1)
ppgplot.pgbox('ABI', 1.0, 10, 'ABI', 0.0, 0)
xPoints = [x - margins for x in range(len(xCollapsed))]
ppgplot.pgsci(2)
ppgplot.pgbin(xPoints, xCollapsed, True)
numPolyPoints = 50
xFit = [float(i) * len(xCollapsed)/numPolyPoints for i in range(numPolyPoints)]
yFit = [xPoly[0]*x*x + xPoly[1]*x + xPoly[2] for x in xFit]
ppgplot.pgsci(3)
ppgplot.pgline([x - margins for x in xFit], yFit)
ppgplot.pgsls(2)
ppgplot.pgline([newxPeak-margins, newxPeak-margins], [yMin, yMax])
ppgplot.pgsci(4)
ppgplot.pgline([xBestOffset, xBestOffset], [yMin, yMax])
ppgplot.pgsls(1)
ppgplot.pgline(xGaussian, xGaussianFit)
# Graph of the y-direction
ppgplot.pgsvp(0.8, 0.9, 0.3, 0.9)
yMax = numpy.max(yCollapsed) * 1.1
if not arg.stacked:
mainPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yUpper = 2.5
yLower = -0.5
for spectrum in spectra:
ppgplot.pgsci(1)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgbin(spectrum.wavelengths, spectrum.flux)
if hasEphemeris:
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%f]"%(spectrum.objectName, spectrum.phase))
else:
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%s]"%(spectrum.objectName, spectrum.loadedFromFilename))
if arg.stacked:
mainPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yLower = -0.5
offset = 2.0
yUpper = numSpectra * offset
lowerWavelength = min(spectrum.wavelengths)
def main():
parser = OptionParser(usage)
parser.add_option("-x", "--xwin", action="store_true", dest="xwin",
default=False, help="Don't make a postscript plot, just use an X-window")
parser.add_option("-p", "--noplot", action="store_false", dest="makeplot",
default=True, help="Look for pulses but do not generate a plot")
parser.add_option("-m", "--maxwidth", type="float", dest="maxwidth", default=0.0,
help="Set the max downsampling in sec (see below for default)")
parser.add_option("-t", "--threshold", type="float", dest="threshold", default=5.0,
help="Set a different threshold SNR (default=5.0)")
parser.add_option("-s", "--start", type="float", dest="T_start", default=0.0,
help="Only plot events occuring after this time (s)")
parser.add_option("-e", "--end", type="float", dest="T_end", default=1e9,
help="Only plot events occuring before this time (s)")
parser.add_option("-g", "--glob", type="string", dest="globexp", default=None,
help="Process the files from this glob expression")
parser.add_option("-f", "--fast", action="store_true", dest="fast",
default=False, help="Use a faster method of de-trending (2x speedup)")
parser.add_option("-b", "--nobadblocks", action="store_false", dest="badblocks",
default=True, help="Don't check for bad-blocks (may save strong pulses)")
parser.add_option("-d", "--detrendlen", type="int", dest="detrendfact", default=1,
help="Chunksize for detrending (pow-of-2 in 1000s)")
(opts, args) = parser.parse_args()
if len(args)==0:
if opts.globexp==None:
print full_usage
sys.exit(0)
else:
args = []
for globexp in opts.globexp.split():
args += glob.glob(globexp)
useffts = True
dosearch = True
if opts.xwin:
pgplot_device = "/XWIN"
else:
pgplot_device = ""
fftlen = 8192 # Should be a power-of-two for best speed
chunklen = 8000 # Must be at least max_downfact less than fftlen
assert(opts.detrendfact in [1,2,4,8,16,32])
detrendlen = opts.detrendfact*1000
if (detrendlen > chunklen):
chunklen = detrendlen
fftlen = int(next2_to_n(chunklen))
blocks_per_chunk = chunklen / detrendlen
overlap = (fftlen - chunklen)/2
worklen = chunklen + 2*overlap # currently it is fftlen...
max_downfact = 30
default_downfacts = [2, 3, 4, 6, 9, 14, 20, 30, 45, 70, 100, 150, 220, 300]
if args[0].endswith(".singlepulse"):
filenmbase = args[0][:args[0].rfind(".singlepulse")]
dosearch = False
elif args[0].endswith(".dat"):
filenmbase = args[0][:args[0].rfind(".dat")]
else:
filenmbase = args[0]
# Don't do a search, just read results and plot
if not dosearch:
info, DMs, candlist, num_v_DMstr = \
read_singlepulse_files(args, opts.threshold, opts.T_start, opts.T_end)
orig_N, orig_dt = int(info.N), info.dt
obstime = orig_N * orig_dt
else:
DMs = []
candlist = []
num_v_DMstr = {}
# Loop over the input files
for filenm in args:
if filenm.endswith(".dat"):
filenmbase = filenm[:filenm.rfind(".dat")]
else:
filenmbase = filenm
info = infodata.infodata(filenmbase+".inf")
DMstr = "%.2f"%info.DM
DMs.append(info.DM)
N, dt = int(info.N), info.dt
obstime = N * dt
# Choose the maximum width to search based on time instead
# of bins. This helps prevent increased S/N when the downsampling
# changes as the DM gets larger.
if opts.maxwidth > 0.0:
downfacts = [x for x in default_downfacts if x*dt <= opts.maxwidth]
else:
downfacts = [x for x in default_downfacts if x <= max_downfact]
if len(downfacts) == 0:
downfacts = [default_downfacts[0]]
if (filenm == args[0]):
orig_N = N
orig_dt = dt
if info.breaks:
offregions = zip([x[1] for x in info.onoff[:-1]],
[x[0] for x in info.onoff[1:]])
# If last break spans to end of file, don't read it in (its just padding)
if offregions[-1][1] == N - 1:
N = offregions[-1][0] + 1
outfile = open(filenmbase+'.singlepulse', mode='w')
# Compute the file length in detrendlens
roundN = N/detrendlen * detrendlen
numchunks = roundN / chunklen
# Read in the file
print 'Reading "%s"...'%filenm
timeseries = Num.fromfile(filenm, dtype=Num.float32, count=roundN)
# Split the timeseries into chunks for detrending
numblocks = roundN/detrendlen
timeseries.shape = (numblocks, detrendlen)
stds = Num.zeros(numblocks, dtype=Num.float64)
# de-trend the data one chunk at a time
print ' De-trending the data and computing statistics...'
for ii, chunk in enumerate(timeseries):
if opts.fast: # use median removal instead of detrending (2x speedup)
tmpchunk = chunk.copy()
tmpchunk.sort()
med = tmpchunk[detrendlen/2]
chunk -= med
tmpchunk -= med
else:
# The detrend calls are the most expensive in the program
timeseries[ii] = scipy.signal.detrend(chunk, type='linear')
tmpchunk = timeseries[ii].copy()
tmpchunk.sort()
# The following gets rid of (hopefully) most of the
# outlying values (i.e. power dropouts and single pulses)
# If you throw out 5% (2.5% at bottom and 2.5% at top)
# of random gaussian deviates, the measured stdev is ~0.871
# of the true stdev. Thus the 1.0/0.871=1.148 correction below.
# The following is roughly .std() since we already removed the median
stds[ii] = Num.sqrt((tmpchunk[detrendlen/40:-detrendlen/40]**2.0).sum() /
(0.95*detrendlen))
stds *= 1.148
# sort the standard deviations and separate those with
# very low or very high values
sort_stds = stds.copy()
sort_stds.sort()
# identify the differences with the larges values (this
# will split off the chunks with very low and very high stds
locut = (sort_stds[1:numblocks/2+1] -
sort_stds[:numblocks/2]).argmax() + 1
hicut = (sort_stds[numblocks/2+1:] -
sort_stds[numblocks/2:-1]).argmax() + numblocks/2 - 2
std_stds = scipy.std(sort_stds[locut:hicut])
median_stds = sort_stds[(locut+hicut)/2]
print " pseudo-median block standard deviation = %.2f" % (median_stds)
if (opts.badblocks):
lo_std = median_stds - 4.0 * std_stds
hi_std = median_stds + 4.0 * std_stds
# Determine a list of "bad" chunks. We will not search these.
bad_blocks = Num.nonzero((stds < lo_std) | (stds > hi_std))[0]
print " identified %d bad blocks out of %d (i.e. %.2f%%)" % \
(len(bad_blocks), len(stds),
100.0*float(len(bad_blocks))/float(len(stds)))
stds[bad_blocks] = median_stds
else:
bad_blocks = []
print " Now searching..."
# Now normalize all of the data and reshape it to 1-D
timeseries /= stds[:,Num.newaxis]
timeseries.shape = (roundN,)
# And set the data in the bad blocks to zeros
# Even though we don't search these parts, it is important
# because of the overlaps for the convolutions
for bad_block in bad_blocks:
loind, hiind = bad_block*detrendlen, (bad_block+1)*detrendlen
timeseries[loind:hiind] = 0.0
# Convert to a set for faster lookups below
bad_blocks = set(bad_blocks)
# Step through the data
dm_candlist = []
for chunknum in xrange(numchunks):
loind = chunknum*chunklen-overlap
hiind = (chunknum+1)*chunklen+overlap
# Take care of beginning and end of file overlap issues
if (chunknum==0): # Beginning of file
chunk = Num.zeros(worklen, dtype=Num.float32)
chunk[overlap:] = timeseries[loind+overlap:hiind]
elif (chunknum==numchunks-1): # end of the timeseries
chunk = Num.zeros(worklen, dtype=Num.float32)
chunk[:-overlap] = timeseries[loind:hiind-overlap]
else:
chunk = timeseries[loind:hiind]
# Make a set with the current block numbers
lowblock = blocks_per_chunk * chunknum
currentblocks = set(Num.arange(blocks_per_chunk) + lowblock)
localgoodblocks = Num.asarray(list(currentblocks -
bad_blocks)) - lowblock
# Search this chunk if it is not all bad
if len(localgoodblocks):
# This is the good part of the data (end effects removed)
goodchunk = chunk[overlap:-overlap]
# need to pass blocks/chunklen, localgoodblocks
# dm_candlist, dt, opts.threshold to cython routine
# Search non-downsampled data first
# NOTE: these nonzero() calls are some of the most
# expensive calls in the program. Best bet would
# probably be to simply iterate over the goodchunk
# in C and append to the candlist there.
hibins = Num.flatnonzero(goodchunk>opts.threshold)
hivals = goodchunk[hibins]
hibins += chunknum * chunklen
hiblocks = hibins/detrendlen
# Add the candidates (which are sorted by bin)
for bin, val, block in zip(hibins, hivals, hiblocks):
if block not in bad_blocks:
time = bin * dt
dm_candlist.append(candidate(info.DM, val, time, bin, 1))
# Now do the downsampling...
for downfact in downfacts:
if useffts:
# Note: FFT convolution is faster for _all_ downfacts, even 2
chunk2 = Num.concatenate((Num.zeros(1000), chunk, Num.zeros(1000)))
goodchunk = Num.convolve(chunk2, Num.ones(downfact), mode='same') / Num.sqrt(downfact)
goodchunk = goodchunk[overlap:-overlap]
#O qualcosa di simile, altrimenti non so perche' trova piu' candidati! Controllare!
else:
# The normalization of this kernel keeps the post-smoothing RMS = 1
kernel = Num.ones(downfact, dtype=Num.float32) / \
Num.sqrt(downfact)
smoothed_chunk = scipy.signal.convolve(chunk, kernel, 1)
goodchunk = smoothed_chunk[overlap:-overlap]
#hibins = Num.nonzero(goodchunk>opts.threshold)[0]
hibins = Num.flatnonzero(goodchunk>opts.threshold)
hivals = goodchunk[hibins]
hibins += chunknum * chunklen
hiblocks = hibins/detrendlen
hibins = hibins.tolist()
hivals = hivals.tolist()
# Now walk through the new candidates and remove those
# that are not the highest but are within downfact/2
# bins of a higher signal pulse
hibins, hivals = prune_related1(hibins, hivals, downfact)
# Insert the new candidates into the candlist, but
# keep it sorted...
for bin, val, block in zip(hibins, hivals, hiblocks):
if block not in bad_blocks:
time = bin * dt
bisect.insort(dm_candlist,
candidate(info.DM, val, time, bin, downfact))
# Now walk through the dm_candlist and remove the ones that
# are within the downsample proximity of a higher
# signal-to-noise pulse
dm_candlist = prune_related2(dm_candlist, downfacts)
print " Found %d pulse candidates"%len(dm_candlist)
# Get rid of those near padding regions
if info.breaks: prune_border_cases(dm_candlist, offregions)
# Write the pulses to an ASCII output file
if len(dm_candlist):
#dm_candlist.sort(cmp_sigma)
outfile.write("# DM Sigma Time (s) Sample Downfact\n")
for cand in dm_candlist:
outfile.write(str(cand))
outfile.close()
# Add these candidates to the overall candidate list
for cand in dm_candlist:
candlist.append(cand)
num_v_DMstr[DMstr] = len(dm_candlist)
if (opts.makeplot):
# Step through the candidates to make a SNR list
DMs.sort()
snrs = []
for cand in candlist:
if not Num.isinf(cand.sigma):
snrs.append(cand.sigma)
if snrs:
maxsnr = max(int(max(snrs)), int(opts.threshold)) + 3
else:
maxsnr = int(opts.threshold) + 3
# Generate the SNR histogram
snrs = Num.asarray(snrs)
(num_v_snr, lo_snr, d_snr, num_out_of_range) = \
scipy.stats.histogram(snrs,
int(maxsnr-opts.threshold+1),
[opts.threshold, maxsnr])
snrs = Num.arange(maxsnr-opts.threshold+1, dtype=Num.float64) * d_snr \
+ lo_snr + 0.5*d_snr
num_v_snr = num_v_snr.astype(Num.float32)
num_v_snr[num_v_snr==0.0] = 0.001
# Generate the DM histogram
num_v_DM = Num.zeros(len(DMs))
for ii, DM in enumerate(DMs):
num_v_DM[ii] = num_v_DMstr["%.2f"%DM]
DMs = Num.asarray(DMs)
# open the plot device
short_filenmbase = filenmbase[:filenmbase.find("_DM")]
if opts.T_end > obstime:
opts.T_end = obstime
if pgplot_device:
ppgplot.pgopen(pgplot_device)
else:
if (opts.T_start > 0.0 or opts.T_end < obstime):
ppgplot.pgopen(short_filenmbase+'_%.0f-%.0fs_singlepulse.ps/VPS'%
(opts.T_start, opts.T_end))
else:
ppgplot.pgopen(short_filenmbase+'_singlepulse.ps/VPS')
ppgplot.pgpap(7.5, 1.0) # Width in inches, aspect
# plot the SNR histogram
ppgplot.pgsvp(0.06, 0.31, 0.6, 0.87)
ppgplot.pgswin(opts.threshold, maxsnr,
Num.log10(0.5), Num.log10(2*max(num_v_snr)))
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCLNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Signal-to-Noise")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Number of Pulses")
ppgplot.pgsch(1.0)
ppgplot.pgbin(snrs, Num.log10(num_v_snr), 1)
# plot the DM histogram
ppgplot.pgsvp(0.39, 0.64, 0.6, 0.87)
# Add [1] to num_v_DM in YMAX below so that YMIN != YMAX when max(num_v_DM)==0
ppgplot.pgswin(min(DMs)-0.5, max(DMs)+0.5, 0.0, 1.1*max(num_v_DM+[1]))
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Number of Pulses")
ppgplot.pgsch(1.0)
ppgplot.pgbin(DMs, num_v_DM, 1)
# plot the SNR vs DM plot
ppgplot.pgsvp(0.72, 0.97, 0.6, 0.87)
ppgplot.pgswin(min(DMs)-0.5, max(DMs)+0.5, opts.threshold, maxsnr)
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-Noise")
ppgplot.pgsch(1.0)
cand_ts = Num.zeros(len(candlist), dtype=Num.float32)
cand_SNRs = Num.zeros(len(candlist), dtype=Num.float32)
cand_DMs = Num.zeros(len(candlist), dtype=Num.float32)
for ii, cand in enumerate(candlist):
cand_ts[ii], cand_SNRs[ii], cand_DMs[ii] = \
cand.time, cand.sigma, cand.DM
ppgplot.pgpt(cand_DMs, cand_SNRs, 20)
# plot the DM vs Time plot
ppgplot.pgsvp(0.06, 0.97, 0.08, 0.52)
ppgplot.pgswin(opts.T_start, opts.T_end, min(DMs)-0.5, max(DMs)+0.5)
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\u-3\d)")
# Circles are symbols 20-26 in increasing order
snr_range = 12.0
cand_symbols = (cand_SNRs-opts.threshold)/snr_range * 6.0 + 20.5
cand_symbols = cand_symbols.astype(Num.int32)
cand_symbols[cand_symbols>26] = 26
for ii in [26, 25, 24, 23, 22, 21, 20]:
inds = Num.nonzero(cand_symbols==ii)[0]
ppgplot.pgpt(cand_ts[inds], cand_DMs[inds], ii)
# Now fill the infomation area
ppgplot.pgsvp(0.05, 0.95, 0.87, 0.97)
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('T', 0.5, 0.0, 0.0,
"Single pulse results for '%s'"%short_filenmbase)
ppgplot.pgsch(0.8)
# first row
ppgplot.pgmtxt('T', -1.1, 0.02, 0.0, 'Source: %s'%\
info.object)
ppgplot.pgmtxt('T', -1.1, 0.33, 0.0, 'RA (J2000):')
ppgplot.pgmtxt('T', -1.1, 0.5, 0.0, info.RA)
ppgplot.pgmtxt('T', -1.1, 0.73, 0.0, 'N samples: %.0f'%orig_N)
# second row
ppgplot.pgmtxt('T', -2.4, 0.02, 0.0, 'Telescope: %s'%\
info.telescope)
ppgplot.pgmtxt('T', -2.4, 0.33, 0.0, 'DEC (J2000):')
ppgplot.pgmtxt('T', -2.4, 0.5, 0.0, info.DEC)
ppgplot.pgmtxt('T', -2.4, 0.73, 0.0, 'Sampling time: %.2f \gms'%\
(orig_dt*1e6))
# third row
if info.instrument.find("pigot") >= 0:
instrument = "Spigot"
else:
instrument = info.instrument
ppgplot.pgmtxt('T', -3.7, 0.02, 0.0, 'Instrument: %s'%instrument)
if (info.bary):
ppgplot.pgmtxt('T', -3.7, 0.33, 0.0, 'MJD\dbary\u: %.12f'%info.epoch)
else:
ppgplot.pgmtxt('T', -3.7, 0.33, 0.0, 'MJD\dtopo\u: %.12f'%info.epoch)
ppgplot.pgmtxt('T', -3.7, 0.73, 0.0, 'Freq\dctr\u: %.1f MHz'%\
((info.numchan/2-0.5)*info.chan_width+info.lofreq))
ppgplot.pgiden()
ppgplot.pgend()
def main():
parser = OptionParser(usage)
parser.add_option(
"-x",
"--xwin",
action="store_true",
dest="xwin",
default=False,
help="Don't make a postscript plot, just use an X-window")
parser.add_option("-p",
"--noplot",
action="store_false",
dest="makeplot",
default=True,
help="Look for pulses but do not generate a plot")
parser.add_option(
"-m",
"--maxwidth",
type="float",
dest="maxwidth",
default=0.0,
help="Set the max downsampling in sec (see below for default)")
parser.add_option("-t",
"--threshold",
type="float",
dest="threshold",
default=5.0,
help="Set a different threshold SNR (default=5.0)")
parser.add_option("-s",
"--start",
type="float",
dest="T_start",
default=0.0,
help="Only plot events occuring after this time (s)")
parser.add_option("-e",
"--end",
type="float",
dest="T_end",
default=1e9,
help="Only plot events occuring before this time (s)")
parser.add_option("-g",
"--glob",
type="string",
dest="globexp",
default=None,
help="Process the files from this glob expression")
parser.add_option("-f",
"--fast",
action="store_true",
dest="fast",
default=False,
help="Use a faster method of de-trending (2x speedup)")
(opts, args) = parser.parse_args()
if len(args) == 0:
if opts.globexp == None:
print full_usage
sys.exit(0)
else:
args = []
for globexp in opts.globexp.split():
args += glob.glob(globexp)
useffts = True
dosearch = True
if opts.xwin:
pgplot_device = "/XWIN"
else:
pgplot_device = ""
fftlen = 8192 # Should be a power-of-two for best speed
chunklen = 8000 # Must be at least max_downfact less than fftlen
detrendlen = 1000 # length of a linear piecewise chunk of data for detrending
blocks_per_chunk = chunklen / detrendlen
overlap = (fftlen - chunklen) / 2
worklen = chunklen + 2 * overlap # currently it is fftlen...
max_downfact = 30
default_downfacts = [2, 3, 4, 6, 9, 14, 20, 30, 45, 70, 100, 150]
if args[0].endswith(".singlepulse"):
filenmbase = args[0][:args[0].rfind(".singlepulse")]
dosearch = False
elif args[0].endswith(".dat"):
filenmbase = args[0][:args[0].rfind(".dat")]
else:
filenmbase = args[0]
# Don't do a search, just read results and plot
if not dosearch:
info, DMs, candlist, num_v_DMstr = \
read_singlepulse_files(args, opts.threshold, opts.T_start, opts.T_end)
orig_N, orig_dt = int(info.N), info.dt
obstime = orig_N * orig_dt
else:
DMs = []
candlist = []
num_v_DMstr = {}
# Loop over the input files
for filenm in args:
if filenm.endswith(".dat"):
filenmbase = filenm[:filenm.rfind(".dat")]
else:
filenmbase = filenm
info = infodata.infodata(filenmbase + ".inf")
DMstr = "%.2f" % info.DM
DMs.append(info.DM)
N, dt = int(info.N), info.dt
obstime = N * dt
# Choose the maximum width to search based on time instead
# of bins. This helps prevent increased S/N when the downsampling
# changes as the DM gets larger.
if opts.maxwidth > 0.0:
downfacts = [
x for x in default_downfacts if x * dt <= opts.maxwidth
]
else:
downfacts = [x for x in default_downfacts if x <= max_downfact]
if len(downfacts) == 0:
downfacts = [default_downfacts[0]]
if (filenm == args[0]):
orig_N = N
orig_dt = dt
if useffts:
fftd_kerns = make_fftd_kerns(downfacts, fftlen)
if info.breaks:
offregions = zip([x[1] for x in info.onoff[:-1]],
[x[0] for x in info.onoff[1:]])
outfile = open(filenmbase + '.singlepulse', mode='w')
# Compute the file length in detrendlens
roundN = N / detrendlen * detrendlen
numchunks = roundN / chunklen
# Read in the file
print 'Reading "%s"...' % filenm
timeseries = Num.fromfile(filenm, dtype=Num.float32, count=roundN)
# Split the timeseries into chunks for detrending
numblocks = roundN / detrendlen
timeseries.shape = (numblocks, detrendlen)
stds = Num.zeros(numblocks, dtype=Num.float64)
# de-trend the data one chunk at a time
print ' De-trending the data and computing statistics...'
for ii, chunk in enumerate(timeseries):
if opts.fast: # use median removal instead of detrending (2x speedup)
tmpchunk = chunk.copy()
tmpchunk.sort()
med = tmpchunk[detrendlen / 2]
chunk -= med
tmpchunk -= med
else:
# The detrend calls are the most expensive in the program
timeseries[ii] = scipy.signal.detrend(chunk, type='linear')
tmpchunk = timeseries[ii].copy()
tmpchunk.sort()
# The following gets rid of (hopefully) most of the
# outlying values (i.e. power dropouts and single pulses)
# If you throw out 5% (2.5% at bottom and 2.5% at top)
# of random gaussian deviates, the measured stdev is ~0.871
# of the true stdev. Thus the 1.0/0.871=1.148 correction below.
# The following is roughly .std() since we already removed the median
stds[ii] = Num.sqrt(
(tmpchunk[detrendlen / 40:-detrendlen / 40]**2.0).sum() /
(0.95 * detrendlen))
stds *= 1.148
# sort the standard deviations and separate those with
# very low or very high values
sort_stds = stds.copy()
sort_stds.sort()
# identify the differences with the larges values (this
# will split off the chunks with very low and very high stds
locut = (sort_stds[1:numblocks / 2 + 1] -
sort_stds[:numblocks / 2]).argmax() + 1
hicut = (sort_stds[numblocks / 2 + 1:] -
sort_stds[numblocks / 2:-1]).argmax() + numblocks / 2 - 2
std_stds = scipy.std(sort_stds[locut:hicut])
median_stds = sort_stds[(locut + hicut) / 2]
lo_std = median_stds - 4.0 * std_stds
hi_std = median_stds + 4.0 * std_stds
# Determine a list of "bad" chunks. We will not search these.
bad_blocks = Num.nonzero((stds < lo_std) | (stds > hi_std))[0]
print " pseudo-median block standard deviation = %.2f" % (
median_stds)
print " identified %d bad blocks out of %d (i.e. %.2f%%)" % \
(len(bad_blocks), len(stds),
100.0*float(len(bad_blocks))/float(len(stds)))
stds[bad_blocks] = median_stds
print " Now searching..."
# Now normalize all of the data and reshape it to 1-D
timeseries /= stds[:, Num.newaxis]
timeseries.shape = (roundN, )
# And set the data in the bad blocks to zeros
# Even though we don't search these parts, it is important
# because of the overlaps for the convolutions
for bad_block in bad_blocks:
loind, hiind = bad_block * detrendlen, (bad_block +
1) * detrendlen
timeseries[loind:hiind] = 0.0
# Convert to a set for faster lookups below
bad_blocks = set(bad_blocks)
# Step through the data
dm_candlist = []
for chunknum in range(numchunks):
loind = chunknum * chunklen - overlap
hiind = (chunknum + 1) * chunklen + overlap
# Take care of beginning and end of file overlap issues
if (chunknum == 0): # Beginning of file
chunk = Num.zeros(worklen, dtype=Num.float32)
chunk[overlap:] = timeseries[loind + overlap:hiind]
elif (chunknum == numchunks - 1): # end of the timeseries
chunk = Num.zeros(worklen, dtype=Num.float32)
chunk[:-overlap] = timeseries[loind:hiind - overlap]
else:
chunk = timeseries[loind:hiind]
# Make a set with the current block numbers
lowblock = blocks_per_chunk * chunknum
currentblocks = set(Num.arange(blocks_per_chunk) + lowblock)
localgoodblocks = Num.asarray(
list(currentblocks - bad_blocks)) - lowblock
# Search this chunk if it is not all bad
if len(localgoodblocks):
# This is the good part of the data (end effects removed)
goodchunk = chunk[overlap:-overlap]
# need to pass blocks/chunklen, localgoodblocks
# dm_candlist, dt, opts.threshold to cython routine
# Search non-downsampled data first
# NOTE: these nonzero() calls are some of the most
# expensive calls in the program. Best bet would
# probably be to simply iterate over the goodchunk
# in C and append to the candlist there.
hibins = Num.flatnonzero(goodchunk > opts.threshold)
hivals = goodchunk[hibins]
hibins += chunknum * chunklen
hiblocks = hibins / detrendlen
# Add the candidates (which are sorted by bin)
for bin, val, block in zip(hibins, hivals, hiblocks):
if block not in bad_blocks:
time = bin * dt
dm_candlist.append(
candidate(info.DM, val, time, bin, 1))
# Prepare our data for the convolution
if useffts: fftd_chunk = rfft(chunk, -1)
# Now do the downsampling...
for ii, downfact in enumerate(downfacts):
if useffts:
# Note: FFT convolution is faster for _all_ downfacts, even 2
goodchunk = fft_convolve(fftd_chunk,
fftd_kerns[ii], overlap,
-overlap)
else:
# The normalization of this kernel keeps the post-smoothing RMS = 1
kernel = Num.ones(downfact, dtype=Num.float32) / \
Num.sqrt(downfact)
smoothed_chunk = scipy.signal.convolve(
chunk, kernel, 1)
goodchunk = smoothed_chunk[overlap:-overlap]
#hibins = Num.nonzero(goodchunk>opts.threshold)[0]
hibins = Num.flatnonzero(goodchunk > opts.threshold)
hivals = goodchunk[hibins]
hibins += chunknum * chunklen
hiblocks = hibins / detrendlen
hibins = hibins.tolist()
hivals = hivals.tolist()
# Now walk through the new candidates and remove those
# that are not the highest but are within downfact/2
# bins of a higher signal pulse
hibins, hivals = prune_related1(
hibins, hivals, downfact)
# Insert the new candidates into the candlist, but
# keep it sorted...
for bin, val, block in zip(hibins, hivals, hiblocks):
if block not in bad_blocks:
time = bin * dt
bisect.insort(
dm_candlist,
candidate(info.DM, val, time, bin,
downfact))
# Now walk through the dm_candlist and remove the ones that
# are within the downsample proximity of a higher
# signal-to-noise pulse
dm_candlist = prune_related2(dm_candlist, downfacts)
print " Found %d pulse candidates" % len(dm_candlist)
# Get rid of those near padding regions
if info.breaks: prune_border_cases(dm_candlist, offregions)
# Write the pulses to an ASCII output file
if len(dm_candlist):
#dm_candlist.sort(cmp_sigma)
outfile.write(
"# DM Sigma Time (s) Sample Downfact\n")
for cand in dm_candlist:
outfile.write(str(cand))
outfile.close()
# Add these candidates to the overall candidate list
for cand in dm_candlist:
candlist.append(cand)
num_v_DMstr[DMstr] = len(dm_candlist)
if (opts.makeplot):
# Step through the candidates to make a SNR list
DMs.sort()
snrs = []
for cand in candlist:
snrs.append(cand.sigma)
if snrs:
maxsnr = max(int(max(snrs)), int(opts.threshold)) + 3
else:
maxsnr = int(opts.threshold) + 3
# Generate the SNR histogram
snrs = Num.asarray(snrs)
(num_v_snr, lo_snr, d_snr, num_out_of_range) = \
scipy.stats.histogram(snrs,
int(maxsnr-opts.threshold+1),
[opts.threshold, maxsnr])
snrs = Num.arange(maxsnr-opts.threshold+1, dtype=Num.float64) * d_snr \
+ lo_snr + 0.5*d_snr
num_v_snr = num_v_snr.astype(Num.float32)
num_v_snr[num_v_snr == 0.0] = 0.001
# Generate the DM histogram
num_v_DM = Num.zeros(len(DMs))
for ii, DM in enumerate(DMs):
num_v_DM[ii] = num_v_DMstr["%.2f" % DM]
DMs = Num.asarray(DMs)
# open the plot device
short_filenmbase = filenmbase[:filenmbase.find("_DM")]
if opts.T_end > obstime:
opts.T_end = obstime
if pgplot_device:
ppgplot.pgopen(pgplot_device)
else:
if (opts.T_start > 0.0 or opts.T_end < obstime):
ppgplot.pgopen(short_filenmbase +
'_%.0f-%.0fs_singlepulse.ps/VPS' %
(opts.T_start, opts.T_end))
else:
ppgplot.pgopen(short_filenmbase + '_singlepulse.ps/VPS')
ppgplot.pgpap(7.5, 1.0) # Width in inches, aspect
# plot the SNR histogram
ppgplot.pgsvp(0.06, 0.31, 0.6, 0.87)
ppgplot.pgswin(opts.threshold, maxsnr, Num.log10(0.5),
Num.log10(2 * max(num_v_snr)))
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCLNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Signal-to-Noise")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Number of Pulses")
ppgplot.pgsch(1.0)
ppgplot.pgbin(snrs, Num.log10(num_v_snr), 1)
# plot the DM histogram
ppgplot.pgsvp(0.39, 0.64, 0.6, 0.87)
# Add [1] to num_v_DM in YMAX below so that YMIN != YMAX when max(num_v_DM)==0
ppgplot.pgswin(
min(DMs) - 0.5,
max(DMs) + 0.5, 0.0, 1.1 * max(num_v_DM + [1]))
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Number of Pulses")
ppgplot.pgsch(1.0)
ppgplot.pgbin(DMs, num_v_DM, 1)
# plot the SNR vs DM plot
ppgplot.pgsvp(0.72, 0.97, 0.6, 0.87)
ppgplot.pgswin(min(DMs) - 0.5, max(DMs) + 0.5, opts.threshold, maxsnr)
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-Noise")
ppgplot.pgsch(1.0)
cand_ts = Num.zeros(len(candlist), dtype=Num.float32)
cand_SNRs = Num.zeros(len(candlist), dtype=Num.float32)
cand_DMs = Num.zeros(len(candlist), dtype=Num.float32)
for ii, cand in enumerate(candlist):
cand_ts[ii], cand_SNRs[ii], cand_DMs[ii] = \
cand.time, cand.sigma, cand.DM
ppgplot.pgpt(cand_DMs, cand_SNRs, 20)
# plot the DM vs Time plot
ppgplot.pgsvp(0.06, 0.97, 0.08, 0.52)
ppgplot.pgswin(opts.T_start, opts.T_end,
min(DMs) - 0.5,
max(DMs) + 0.5)
ppgplot.pgsch(0.8)
ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\u-3\d)")
# Circles are symbols 20-26 in increasing order
snr_range = 12.0
cand_symbols = (cand_SNRs - opts.threshold) / snr_range * 6.0 + 20.5
cand_symbols = cand_symbols.astype(Num.int32)
cand_symbols[cand_symbols > 26] = 26
for ii in [26, 25, 24, 23, 22, 21, 20]:
inds = Num.nonzero(cand_symbols == ii)[0]
ppgplot.pgpt(cand_ts[inds], cand_DMs[inds], ii)
# Now fill the infomation area
ppgplot.pgsvp(0.05, 0.95, 0.87, 0.97)
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('T', 0.5, 0.0, 0.0,
"Single pulse results for '%s'" % short_filenmbase)
ppgplot.pgsch(0.8)
# first row
ppgplot.pgmtxt('T', -1.1, 0.02, 0.0, 'Source: %s'%\
info.object)
ppgplot.pgmtxt('T', -1.1, 0.33, 0.0, 'RA (J2000):')
ppgplot.pgmtxt('T', -1.1, 0.5, 0.0, info.RA)
ppgplot.pgmtxt('T', -1.1, 0.73, 0.0, 'N samples: %.0f' % orig_N)
# second row
ppgplot.pgmtxt('T', -2.4, 0.02, 0.0, 'Telescope: %s'%\
info.telescope)
ppgplot.pgmtxt('T', -2.4, 0.33, 0.0, 'DEC (J2000):')
ppgplot.pgmtxt('T', -2.4, 0.5, 0.0, info.DEC)
ppgplot.pgmtxt('T', -2.4, 0.73, 0.0, 'Sampling time: %.2f \gms'%\
(orig_dt*1e6))
# third row
if info.instrument.find("pigot") >= 0:
instrument = "Spigot"
else:
instrument = info.instrument
ppgplot.pgmtxt('T', -3.7, 0.02, 0.0, 'Instrument: %s' % instrument)
if (info.bary):
ppgplot.pgmtxt('T', -3.7, 0.33, 0.0,
'MJD\dbary\u: %.12f' % info.epoch)
else:
ppgplot.pgmtxt('T', -3.7, 0.33, 0.0,
'MJD\dtopo\u: %.12f' % info.epoch)
ppgplot.pgmtxt('T', -3.7, 0.73, 0.0, 'Freq\dctr\u: %.1f MHz'%\
((info.numchan/2-0.5)*info.chan_width+info.lofreq))
ppgplot.pgiden()
ppgplot.pgend()
yUpper = 2.5
yLower = -0.5
fitPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
for spectrum in spectra:
ppgplot.pgslct(mainPGPlotWindow)
ppgplot.pgsci(1)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgbin(spectrum.wavelengths, spectrum.flux)
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%s]"%(spectrum.objectName, spectrum.loadedFromFilename))
# Grab the continuum from either side of the spectrum
lowerCut = 6540
upperCut = 6585
continuumSpectrum = copy.deepcopy(spectrum)
continuumSpectrum.snipWavelengthRange(lowerCut, upperCut)
ppgplot.pgsci(2)
ppgplot.pgbin(continuumSpectrum.wavelengths, continuumSpectrum.flux)
# Now fit a polynomial to continuum around the doublet
a0 = 0.0 # Constant term
a0 = numpy.mean(continuumSpectrum.flux)
a1 = 0.0 # Linear term
def main(args):
with open(os.path.join(os.path.dirname(__file__),
'precisiondata.cpickle')) as filedata:
exptimes, crosspoints, satpoints = pickle.load(filedata)
x_range = [9, 14]
interpcross = interp1d(exptimes, crosspoints, kind='linear')
interpsat = interp1d(exptimes, satpoints, kind='linear')
N = 5
colours = np.arange(2, 2 + N, 1)
exptimes = np.arange(1, N + 1) * 10
if args.besancon:
all_vmags = get_besancon_mag_data()
yhigh = 0.3
title = 'Besancon'
else:
all_vmags = get_nomad_mag_data()
yhigh = 0.4
title = 'NOMAD'
ytot = yhigh * len(all_vmags)
with pgh.open_plot(args.output):
pg.pgvstd()
pg.pgswin(x_range[0], x_range[1], 0, yhigh)
for exptime, colour in zip(exptimes, colours):
satpoint = interpsat(exptime)
crosspoint = interpcross(exptime)
selected = all_vmags[(all_vmags > satpoint) & (all_vmags <=
crosspoint)]
print(exptime, len(selected))
xdata, ydata = cumulative_hist(np.array(selected),
min_val=x_range[0], max_val=x_range[1], norm=len(all_vmags))
ydata /= float(len(all_vmags))
with pgh.change_colour(colour):
pg.pgbin(xdata, ydata, False)
pg.pgbox('bcnst', 0, 0, 'bcnst', 0, 0)
pg.pglab(r'V magnitude', 'High precision fraction', title)
# Label the right hand side
pg.pgswin(x_range[0], x_range[1], 0, ytot)
pg.pgbox('', 0, 0, 'smt', 0, 0)
pg.pgmtxt('r', 2., 0.5, 0.5, 'N')
# Create the legend
pg.pgsvp(0.7, 0.9, 0.1, 0.3)
pg.pgswin(0., 1., 0., 1.)
for i, (exptime, colour) in enumerate(zip(exptimes, colours)):
yval = 0.1 + 0.8 * i / len(exptimes)
with pgh.change_colour(colour):
pg.pgline(np.array([0.2, 0.4]), np.ones(2) * yval)
pg.pgtext(0.5, yval, r'{:d} s'.format(exptime))
yLower = -0.5
fitPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
for spectrum in spectra:
ppgplot.pgslct(mainPGPlotWindow)
ppgplot.pgsci(1)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
lowerLimit = min(spectrum.fluxErrors)
ppgplot.pgenv(lowerWavelength, upperWavelength, 0, upperFlux, 0, 0)
ppgplot.pgbin(spectrum.wavelengths, spectrum.flux)
ppgplot.pgbin(spectrum.wavelengths, spectrum.fluxErrors)
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%s]"%(spectrum.objectName, spectrum.loadedFromFilename))
# Grab the continuum from either side of the spectrum
lowerCut = arg.fl
upperCut = arg.fu
continuumSpectrum = copy.deepcopy(spectrum)
continuumSpectrum.snipWavelengthRange(lowerCut, upperCut)
ppgplot.pgsci(2)
ppgplot.pgbin(continuumSpectrum.wavelengths, continuumSpectrum.flux)
# Now fit a polynomial to continuum around the doublet
a0 = 0.0 # Constant term
a0 = numpy.mean(continuumSpectrum.flux)
def plot_rating_sheet(rating):
"""
Plot a fact sheet on the ratings in the database corresponding to 'rating'.
'rating' is a dictionary of information from the MySQL database (as returned
by 'get_all_rating_types()'.
"""
plot_utils.beginplot("rating_report%s.ps" % currdatetime.strftime('%y%m%d'), vertical=True)
ch0 = ppgplot.pgqch()
ppgplot.pgsch(0.5)
ch = ppgplot.pgqch()
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 1 of 2" % (rating["name"], currdatetime.strftime('%c')))
ppgplot.pgsch(ch)
# Plot Histograms
all_ratings = get_ratings(rating["rating_id"])
range = xmin,xmax = np.min(all_ratings), np.max(all_ratings)
ppgplot.pgsci(1)
ppgplot.pgslw(1)
#===== Total/Classified/Unclassified
# Get data
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
(tot_counts, tot_left_edges)=np.histogram(all_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(tot_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, tot_counts)
(clsfd_counts, clsfd_left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3,4,5,6,7)), bins=NUMBINS, range=range)
ppgplot.pgsci(3) # plot classified in green
ppgplot.pgbin(tot_left_edges, clsfd_counts)
unclsfd_counts = tot_counts-clsfd_counts
ppgplot.pgsci(2) # plot unclassified in red
ppgplot.pgbin(tot_left_edges, unclsfd_counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Counts","")
#===== Class 1/2/3
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
(counts, left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3)), bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Class 1/2/3","")
#===== RFI
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
rfi_ratings = get_ratings(rating["rating_id"], human_classification=(4,))
(counts, left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI","")
#===== Noise
ppgplot.pgsvp(0.1, 0.9, 0.3, 0.45)
noise_ratings = get_ratings(rating["rating_id"], human_classification=(5,))
(counts, left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Noise","")
#===== Known/Harmonic
ppgplot.pgsvp(0.1, 0.9, 0.15, 0.3)
known_ratings = get_ratings(rating["rating_id"], human_classification=(6,7))
(counts, left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known/Harmonic","")
#===== Second page for differential histograms
plot_utils.nextpage(vertical=True)
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 2 of 2" % (rating["name"], currdatetime.strftime('%c')))
#===== RFI - Known
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
if rfi_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - known_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
#===== RFI - Noise
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
if noise_ratings.size==0 or rfi_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
#===== Known - Noise
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
if noise_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
# Y-axis label is taken care of outside of if/else (below)
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = known_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgswin(xmin,xmax,0,1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("NTS",0,5,"",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known - Noise","")
ppgplot.pgsch(ch0) # reset character height
