def plot_header(fname, ff, iod_line):
# ppgplot arrays
heat_l = np.array([0.0, 0.2, 0.4, 0.6, 1.0])
heat_r = np.array([0.0, 0.5, 1.0, 1.0, 1.0])
heat_g = np.array([0.0, 0.0, 0.5, 1.0, 1.0])
heat_b = np.array([0.0, 0.0, 0.0, 0.3, 1.0])
# Plot
ppg.pgopen(fname)
ppg.pgpap(0.0, 1.0)
ppg.pgsvp(0.1, 0.95, 0.1, 0.8)
ppg.pgsch(0.8)
ppg.pgmtxt("T", 6.0, 0.0, 0.0,
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
if is_calibrated(ff):
ppg.pgsci(1)
else:
ppg.pgsci(2)
ppg.pgmtxt(
"T", 4.8, 0.0, 0.0, "R.A.: %10.5f (%4.1f'') Decl.: %10.5f (%4.1f'')" %
(ff.crval[0], 3600.0 * ff.crres[0], ff.crval[1], 3600.0 * ff.crres[1]))
ppg.pgsci(1)
ppg.pgmtxt("T", 3.6, 0.0, 0.0, ("FoV: %.2f\\(2218)x%.2f\\(2218) "
"Scale: %.2f''x%.2f'' pix\\u-1\\d") %
(ff.wx, ff.wy, 3600.0 * ff.sx, 3600.0 * ff.sy))
ppg.pgmtxt(
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
(np.mean(ff.zmax), np.std(ff.zmax), ff.zmaxmin, ff.zmaxmax))
ppg.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
ppg.pgsch(1.0)
ppg.pgwnad(0.0, ff.nx, 0.0, ff.ny)
ppg.pglab("x (pix)", "y (pix)", " ")
ppg.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
def ok():
if not with_pg:
return True
pg.pgopen('/null')
self.ccd.plot(5, 95)
pg.pgclos()
return True
def ok():
if not with_pg:
return True
pg.pgopen('/null')
self.ccd.plot(5,95)
pg.pgclos()
return True
def createPGplotWindow(handle, width, height):
""" Set up the PGPLOT windows """
newPlot = {}
newPlot["pgplotHandle"] = ppgplot.pgopen("/xs")
ppgplot.pgpap(2, 1)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
return newPlot
def createPGplotWindow(handle, width, height):
""" Set up the PGPLOT windows """
newPlot = {}
newPlot['pgplotHandle'] = ppgplot.pgopen('/xs')
ppgplot.pgpap(2, 1)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
return newPlot
def drawMask(mask):
print ("Drawing the mask.")
if "pgplotHandle" not in maskPlot.keys():
maskPlot["pgplotHandle"] = ppgplot.pgopen("/xs")
maskPlot["pgPlotTransform"] = [0, 1, 0, 0, 0, 1]
else:
ppgplot.pgslct(maskPlot["pgplotHandle"])
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
ppgplot.pggray(mask, 0, width - 1, 0, height - 1, 0, 255, maskPlot["pgPlotTransform"])
ppgplot.pgslct(imagePlot["pgplotHandle"])
def drawMask(mask):
print("Drawing the mask.")
if 'pgplotHandle' not in maskPlot.keys():
maskPlot['pgplotHandle'] = ppgplot.pgopen('/xs')
maskPlot['pgPlotTransform'] = [0, 1, 0, 0, 0, 1]
else:
ppgplot.pgslct(maskPlot['pgplotHandle'])
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
ppgplot.pggray(mask, 0, width - 1, 0, height - 1, 0, 255,
maskPlot['pgPlotTransform'])
ppgplot.pgslct(imagePlot['pgplotHandle'])
def openDev(type='/XWINDOW'):
"""
DES: open a device, return the device id
INP: (string) type = pgplot device type
"""
global CurrentDev, DevList
if isinstance(type, str):
CurrentDev = ppgplot.pgopen(type)
ppgplot.pgask(0)
DevList.append(CurrentDev)
else:
__MessHand.setMess(0, "openDev: string argument required ")
__queryDev()
def pgplot(slot, plotterHandle):
print "We are about to plot: %s"%(slot)
lightcurveView = ppgplot.pgopen('/xs')
xValues = slot.photometry.times
xValues = numpy.arange(0, len(xValues))
yValues = slot.getColumn("Counts")
ppgplot.pgenv(min(xValues), max(xValues), min(yValues), max(yValues), 0, 0)
ppgplot.pgask(False)
ppgplot.pgsci(2)
ppgplot.pgpt(xValues, yValues, 1)
ppgplot.pgclos()
return
def startPlotter(self):
if self.plotDeviceIsOpened:
raise ValueError("You already started a plot!")
devId = pgplot.pgopen(self.deviceName)
self.plotDeviceIsOpened = True
if not self.widthInches is None:
pgplot.pgpap(self.widthInches, self.yOnXRatio)
# For devices /xs, /xw, /png etc, should make the paper white and the ink black. Only for /ps does pgplot default to that.
#
deviceWithoutFile = self.deviceName.split('/')[-1]
if deviceWithoutFile == 'xs' or deviceWithoutFile == 'xw' or deviceWithoutFile == 'png':
pgplot.pgscr(0, 1.0, 1.0, 1.0)
pgplot.pgscr(1, 0.0, 0.0, 0.0)
pgplot.pgsvp(self._vXLo, self._vXHi, self._vYLo, self._vYHi)
if self.fixAspect:
pgplot.pgwnad(self.worldXLo, self.worldXHi, self.worldYLo,
self.worldYHi)
else:
pgplot.pgswin(self.worldXLo, self.worldXHi, self.worldYLo,
self.worldYHi)
pgplot.pgsfs(2)
pgplot.pgslw(1)
pgplot.pgsch(self._charHeight)
self._setColourRepresentations()
# Set up things so calling pgplot.pggray() won't overwrite the CR of any of the colours in self.colours.
#
(minCI, maxCI) = pgplot.pgqcir()
if minCI <= self.maxCI:
pgplot.pgscir(self.maxCI + 1, maxCI)
(xLoPixels, xHiPixels, yLoPixels, yHiPixels) = pgplot.pgqvsz(3)
(xLoInches, xHiInches, yLoInches, yHiInches) = pgplot.pgqvsz(1)
self.xPixelWorld = (xHiInches - xLoInches) / (xHiPixels - xLoPixels)
self.yPixelWorld = (yHiInches - yLoInches) / (yHiPixels - yLoPixels)
if r.has_mask: print "Mask found"
spectrum = spectrumClasses.spectrumObject()
npoints = spectrum.setData(wavelengths, flux)
targetName = spectrum.parseHeaderInfo(head)
print "Parsed headers of", targetName
print r.oneLine()
if fileIndex == 0:
spectra.append(spectrum)
else:
spectra[index].appendNewData(spectrum)
numSpectra = len(spectra)
print "%d spectra have been loaded."%numSpectra
mainPGPlotWindow = ppgplot.pgopen('/xs')
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgask(False)
ppgplot.pglab("wavelength", "flux", "spectrum")
phases = []
rvs = []
wvshifts = []
wvshiftErrors = []
previousWavelengthFit = 8173.0
for spectrum in spectra:
spectrum.trimWavelengthRange(8000, 8400)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
ppgplot.pgsci(1)
#/usr/bin/env python
#
# pgex1: freely taken after PGDEMO1.F
#
import ppgplot, Numeric
import sys
# create an array
xs=Numeric.array([1.,2.,3.,4.,5.])
ys=Numeric.array([1.,4.,9.,16.,25.])
# creat another array
yr = 0.1*Numeric.array(range(0,60))
xr = yr*yr
# pgplotting
if len(sys.argv) > 1: # if we got an argument use the argument as devicename
ppgplot.pgopen(sys.argv[1])
else:
ppgplot.pgopen('?')
ppgplot.pgenv(0.,10.,0.,20.,0,1)
ppgplot.pglab('(x)', '(y)', 'PGPLOT Example 1: y = x\u2')
ppgplot.pgpt(xs,ys,9)
ppgplot.pgline(xr,yr)
ppgplot.pgclos()
def plot(self, vlo=2., vhi=98., nc=-1, method='p', mpl=False, cmap=CMDEF, \
close=True, x1=None, x2=None, y1=None, y2=None, sepmin=1.):
"""
Plots an MCCD using pgplot or matplotlib if preferred.
:Parameters:
vlo : float
number specifying the lowest level to plot (default as a percentile)
vhi : float
number specifying the lowest level to plot (default as a percentile)
nc : int
CCD number (starting from 0, -1 for all)
method : string
how vlo and vhi are to be interpreted. 'p' = percentile, 'a' = automatic (min to max,
vlo and vhi are irrelevant), 'd' = direct, i.e. just take the values given.
mpl : bool
True to prefer matplotlib over pgplot (which may not even be an option)
cmap : matplotlib.cm.binary
colour map if using matplotlib
close : bool
close (pgplot) or 'show' (matplotlib) the plot at the end (or not, to allow
you to plot something else, use a cursor etc). In the case of pgplot, this also
implies opening the plot at the start, i.e. a self-contained quick plot.
x1 : float
left-hand plot limit. Defaults to 0.5
x2 : float
right-hand plot limit. Defaults to nxmax+0.5
y1 : float
lower plot limit. Defaults to 0.5
y2 : float
upper plot limit. Defaults to nymax+0.5
sepmin : float
minimum separation between intensity limits (> 0 to stop PGPLOT complaining)
:Returns:
range(s) : tuple or list
the plot range(s) used either as a single 2-element tuple, or
a list of them, one per CCD plotted.
"""
if nc == -1:
nc1 = 0
nc2 = len(self)
else:
nc1 = nc
nc2 = nc + 1
if not mpl:
if close: pg.pgopen('/xs')
if nc2 - nc1 > 1: pg.pgsubp(nc2 - nc1, 1)
prange = []
for nc, ccd in enumerate(self._data[nc1:nc2]):
# Determine intensity range to display
if method == 'p':
vmin, vmax = ccd.centile((vlo, vhi))
elif method == 'a':
vmin, vmax = ccd.min(), ccd.max()
elif method == 'd':
vmin, vmax = vlo, vhi
else:
raise UltracamError(
'MCCD.plot: method must be one of p, a or d.')
if vmin == vmax:
vmin -= sepmin / 2.
vmax += sepmin / 2.
prange.append((vmin, vmax))
# start
nxmax, nymax = ccd.nxmax, ccd.nymax
x1 = 0.5 if x1 is None else x1
x2 = nxmax + 0.5 if x2 is None else x2
y1 = 0.5 if y1 is None else y1
y2 = nymax + 0.5 if y2 is None else y2
if mpl:
if nc2 - nc1 > 1:
plt.subplot(1, nc2 - nc1, nc + 1)
plt.axis('equal')
else:
if nc2 - nc1 > 1: pg.pgpanl(nc - nc1 + 1, 1)
pg.pgwnad(x1, x2, y1, y2)
# plot CCD
ccd.plot(vmin, vmax, mpl, cmap)
# per-frame finishing-off
if mpl:
plt.xlim(x1, x2)
plt.ylim(y1, y2)
else:
pg.pgbox('bcnst', 0, 0, 'bcnst', 0, 0)
pg.pglab('X', 'Y', '')
if close:
if mpl:
plt.show()
else:
pg.pgclos()
# return intensity range(s) used
if len(prange) == 1:
return prange[0]
else:
return tuple(prange)
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()
xLabel = xColumn yLabel = "flux ratio" # Find the best y-limits lowerY = 1E8 upperY = -1E8 for photometry in allData: yData = photometry[yColumn] if max(yData)>upperY: upperY = max(yData) if min(yData)<lowerY: lowerY = min(yData) # Initialise the plot environment plotDevices = ["/xs"] for plotDevice in plotDevices: mainPGPlotWindow = ppgplot.pgopen(plotDevice) pgPlotTransform = [0, 1, 0, 0, 0, 1] ppgplot.pgpap(10, 0.618) ppgplot.pgask(arg.ask) for index, photometry in enumerate(allData): x_values = photometry[xColumn] y_values = photometry[yColumn] y_errors = photometry[yErrors] x_lower, x_upper = (min(x_values), max(x_values)) numpoints = len(x_values) if "JD" in xColumn: x_offset = int(x_lower) x_values = [(x-x_offset) for x in x_values] xLabel= xColumn + " - %d"%x_lower ppgplot.pgsci(1)
sys.exit()
phase = phaseAngle
# Snip out Halpha
spectrum.snipWavelengthRange(6550, 6570)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
observedSpectrumRange = (lowerWavelength, upperWavelength)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
lowerFlux = 0
mainPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
ppgplot.pglab("wavelength", "flux", spectrum.objectName)
observedArea = spectrum.integrate()
print "Wavelength range of observations:", observedSpectrumRange
# modelPlotWindow = ppgplot.pgopen(arg.device)
# pgPlotTransform = [0, 1, 0, 0, 0, 1]
# ppgplot.pgask(False)
currentPlotWindow = ppgplot.pgopen(arg.device)
filter = card.header[key]
imageData = hdulist[1].data
savedImageData = numpy.copy(imageData)
wcsSolution = WCS(hdulist[1].header)
hdulist.close()
(height, width) = numpy.shape(imageData)
aspectRatio = float(height) / float(width)
print(aspectRatio)
""" Set up the PGPLOT windows """
imagePlot = {}
imagePlot['pgplotHandle'] = ppgplot.pgopen('/xs')
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
# ppgplot.pgenv(0., width,0., height, 1, -2)
imagePlot['pgPlotTransform'] = [0, 1, 0, 0, 0, 1]
boostedImage = generalUtils.percentiles(imageData, 20, 99)
ppgplot.pggray(boostedImage, 0, width - 1, 0, height - 1, 0, 255,
imagePlot['pgPlotTransform'])
# Determine the RA, DEC of the centre of the image, using the WCS solution found in the FITS header
imageCentre = [width / 2, height / 2]
ra, dec = wcsSolution.all_pix2world([imageCentre], 1)[0]
target.appendData(data)
ptfFile.close()
objects.append(target)
print "%d targets loaded"%len(objects)
#for o in objects:
# o.filterData('mag', 10, 16.5)
if arg.ps: device = "lightcurves.ps/ps"
else: device = "/xs"
PGPlotWindow = ppgplot.pgopen(device)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(PGPlotWindow)
ppgplot.pgsci(1)
ppgplot.pgask(False)
for index, o in enumerate(objects):
HJD = o.getColumn('HJD')
mag = o.getColumn('mag')
err = o.getColumn('err')
startDate = numpy.min(HJD)
dates = [ d - startDate for d in HJD]
endDate = numpy.max(HJD)
magMax = numpy.max(mag) + err[numpy.argmax(mag)]
magMin = numpy.min(mag) - err[numpy.argmin(mag)]
meanError = numpy.mean(err)
print "%s Start date: %f, End date: %f"%(o.id, startDate, endDate)
def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin):
# Read four frame
ff = fourframe(fname)
# Skip saturated frames
if np.sum(ff.zavg > 240.0) / float(ff.nx * ff.ny) > 0.95:
return
# Read satelite IDs
try:
f = open(fname + ".id")
except OSError:
print("Cannot open", fname + ".id")
else:
lines = f.readlines()
f.close()
# ppgplot arrays
tr = np.array([-0.5, 1.0, 0.0, -0.5, 0.0, 1.0])
heat_l = np.array([0.0, 0.2, 0.4, 0.6, 1.0])
heat_r = np.array([0.0, 0.5, 1.0, 1.0, 1.0])
heat_g = np.array([0.0, 0.0, 0.5, 1.0, 1.0])
heat_b = np.array([0.0, 0.0, 0.0, 0.3, 1.0])
# Loop over identifications
for line in lines:
# Decode
id = satid(line)
# Skip slow moving objects
drdt = np.sqrt(id.dxdt**2 + id.dydt**2)
if drdt < drdtmin:
continue
# Extract significant pixels
x, y, t, sig = ff.significant(trksig, id.x0, id.y0, id.dxdt, id.dydt,
trkrmin)
# Fit tracks
if len(t) > ntrkmin:
# Get times
tmin = np.min(t)
tmax = np.max(t)
tmid = 0.5 * (tmax + tmin)
mjd = ff.mjd + tmid / 86400.0
# Skip if no variance in time
if np.std(t - tmid) == 0.0:
continue
# Very simple polynomial fit; no weighting, no cleaning
px = np.polyfit(t - tmid, x, 1)
py = np.polyfit(t - tmid, y, 1)
# Extract results
x0, y0 = px[1], py[1]
dxdt, dydt = px[0], py[0]
xmin = x0 + dxdt * (tmin - tmid)
ymin = y0 + dydt * (tmin - tmid)
xmax = x0 + dxdt * (tmax - tmid)
ymax = y0 + dydt * (tmax - tmid)
cospar = get_cospar(id.norad)
obs = observation(ff, mjd, x0, y0)
iod_line = "%s" % format_iod_line(id.norad, cospar, ff.site_id,
obs.nfd, obs.ra, obs.de)
print(iod_line)
if id.catalog.find("classfd.tle") > 0:
outfname = "classfd.dat"
elif id.catalog.find("inttles.tle") > 0:
outfname = "inttles.dat"
else:
outfname = "catalog.dat"
f = open(outfname, "a")
f.write("%s\n" % iod_line)
f.close()
# Plot
ppgplot.pgopen(
fname.replace(".fits", "") + "_%05d.png/png" % id.norad)
#ppgplot.pgopen("/xs")
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgsvp(0.1, 0.95, 0.1, 0.8)
ppgplot.pgsch(0.8)
ppgplot.pgmtxt(
"T", 6.0, 0.0, 0.0,
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
if (3600.0 * ff.crres[0] < 1e-3
) | (3600.0 * ff.crres[1] < 1e-3) | (
ff.crres[0] / ff.sx > 2.0) | (ff.crres[1] / ff.sy > 2.0):
ppgplot.pgsci(2)
else:
ppgplot.pgsci(1)
ppgplot.pgmtxt(
"T", 4.8, 0.0, 0.0,
"R.A.: %10.5f (%4.1f'') Decl.: %10.5f (%4.1f'')" %
(ff.crval[0], 3600.0 * ff.crres[0], ff.crval[1],
3600.0 * ff.crres[1]))
ppgplot.pgsci(1)
ppgplot.pgmtxt(
"T", 3.6, 0.0, 0.0,
"FoV: %.2f\\(2218)x%.2f\\(2218) Scale: %.2f''x%.2f'' pix\\u-1\\d"
% (ff.wx, ff.wy, 3600.0 * ff.sx, 3600.0 * ff.sy))
ppgplot.pgmtxt(
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
(np.mean(ff.zmax), np.std(ff.zmax), ff.vmin, ff.vmax))
ppgplot.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
ppgplot.pgsch(1.0)
ppgplot.pgwnad(0.0, ff.nx, 0.0, ff.ny)
ppgplot.pglab("x (pix)", "y (pix)", " ")
ppgplot.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
ppgplot.pgimag(ff.zmax, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1,
ff.vmax, ff.vmin, tr)
ppgplot.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
ppgplot.pgstbg(1)
ppgplot.pgsci(0)
if id.catalog.find("classfd.tle") > 0:
ppgplot.pgsci(4)
elif id.catalog.find("inttles.tle") > 0:
ppgplot.pgsci(3)
ppgplot.pgpt(np.array([x0]), np.array([y0]), 4)
ppgplot.pgmove(xmin, ymin)
ppgplot.pgdraw(xmax, ymax)
ppgplot.pgsch(0.65)
ppgplot.pgtext(np.array([x0]), np.array([y0]), " %05d" % id.norad)
ppgplot.pgsch(1.0)
ppgplot.pgsci(1)
ppgplot.pgend()
elif id.catalog.find("classfd.tle") > 0:
# Track and stack
t = np.linspace(0.0, ff.texp)
x, y = id.x0 + id.dxdt * t, id.y0 + id.dydt * t
c = (x > 0) & (x < ff.nx) & (y > 0) & (y < ff.ny)
# Skip if no points selected
if np.sum(c) == 0:
continue
# Compute track
tmid = np.mean(t[c])
mjd = ff.mjd + tmid / 86400.0
xmid = id.x0 + id.dxdt * tmid
ymid = id.y0 + id.dydt * tmid
ztrk = ndimage.gaussian_filter(ff.track(id.dxdt, id.dydt, tmid),
1.0)
vmin = np.mean(ztrk) - 2.0 * np.std(ztrk)
vmax = np.mean(ztrk) + 6.0 * np.std(ztrk)
# Select region
xmin = int(xmid - 100)
xmax = int(xmid + 100)
ymin = int(ymid - 100)
ymax = int(ymid + 100)
if xmin < 0: xmin = 0
if ymin < 0: ymin = 0
if xmax > ff.nx: xmax = ff.nx - 1
if ymax > ff.ny: ymax = ff.ny - 1
# Find peak
x0, y0, w, sigma = peakfind(ztrk[ymin:ymax, xmin:xmax])
x0 += xmin
y0 += ymin
# Skip if peak is not significant
if sigma < trksig:
continue
# Skip if point is outside selection area
if inside_selection(id, xmid, ymid, x0, y0) == False:
continue
# Format IOD line
cospar = get_cospar(id.norad)
obs = observation(ff, mjd, x0, y0)
iod_line = "%s" % format_iod_line(id.norad, cospar, ff.site_id,
obs.nfd, obs.ra, obs.de)
print(iod_line)
if id.catalog.find("classfd.tle") > 0:
outfname = "classfd.dat"
elif id.catalog.find("inttles.tle") > 0:
outfname = "inttles.dat"
else:
outfname = "catalog.dat"
f = open(outfname, "a")
f.write("%s\n" % iod_line)
f.close()
# Plot
ppgplot.pgopen(
fname.replace(".fits", "") + "_%05d.png/png" % id.norad)
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgsvp(0.1, 0.95, 0.1, 0.8)
ppgplot.pgsch(0.8)
ppgplot.pgmtxt(
"T", 6.0, 0.0, 0.0,
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
ppgplot.pgmtxt(
"T", 4.8, 0.0, 0.0,
"R.A.: %10.5f (%4.1f'') Decl.: %10.5f (%4.1f'')" %
(ff.crval[0], 3600.0 * ff.crres[0], ff.crval[1],
3600.0 * ff.crres[1]))
ppgplot.pgmtxt(
"T", 3.6, 0.0, 0.0,
"FoV: %.2f\\(2218)x%.2f\\(2218) Scale: %.2f''x%.2f'' pix\\u-1\\d"
% (ff.wx, ff.wy, 3600.0 * ff.sx, 3600.0 * ff.sy))
ppgplot.pgmtxt(
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
(np.mean(ff.zmax), np.std(ff.zmax), ff.vmin, ff.vmax))
ppgplot.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
ppgplot.pgsch(1.0)
ppgplot.pgwnad(0.0, ff.nx, 0.0, ff.ny)
ppgplot.pglab("x (pix)", "y (pix)", " ")
ppgplot.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
ppgplot.pgimag(ztrk, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1,
vmax, vmin, tr)
ppgplot.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
ppgplot.pgstbg(1)
plot_selection(id, xmid, ymid)
ppgplot.pgsci(0)
if id.catalog.find("classfd.tle") > 0:
ppgplot.pgsci(4)
elif id.catalog.find("inttles.tle") > 0:
ppgplot.pgsci(3)
ppgplot.pgpt(np.array([id.x0]), np.array([id.y0]), 17)
ppgplot.pgmove(id.x0, id.y0)
ppgplot.pgdraw(id.x1, id.y1)
ppgplot.pgpt(np.array([x0]), np.array([y0]), 4)
ppgplot.pgsch(0.65)
ppgplot.pgtext(np.array([id.x0]), np.array([id.y0]),
" %05d" % id.norad)
ppgplot.pgsch(1.0)
ppgplot.pgsci(1)
ppgplot.pgend()
images = []
for f in filesToLoad:
image = Image.open(f)
print image.bits, image.size, image.format, image.mode
exposureTime = getExifValue(image, 'ExposureTime')
print "Exposure time:", exposureTime
if useDark:
image = PIL.ImageChops.difference(image, dark)
images.append(image)
(width, height) = images[0].size
previewWindow = ppgplot.pgopen('/xs')
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(0, width-1, 0, height-1, 0, 0)
outputWindow = ppgplot.pgopen('/xs')
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(0, width-1, 0, height-1, 0, 0)
redReference = numpy.array(images[0].split()[0])
redTotal = numpy.zeros(numpy.shape(redReference))
greenTotal = numpy.zeros(numpy.shape(redReference))
blueTotal = numpy.zeros(numpy.shape(redReference))
rgx = num.asarray([rs[0], rs[np-1]])
rgy = num.asarray([zs[0], zs[np-1]])
freqcut = pffdot[np/2, :]
fdotcut = pffdot[:, np/2]
image='antirainbow'
device='ffdot_combined.eps/VCPS'
device='/XWIN'
labx='Fourier Frequency Offset (bins)'
laby='Fourier Frequency Derivative (bins)'
contours = num.asarray([0.1, 0.3, 0.5, 0.7, 0.9])
imfract = 0.65
margin = 0.08
ppgplot.pgopen(device)
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgpage()
# Give z and w values and power change
ppgplot.pgsvp(margin+imfract, 1.0-margin/2, margin+imfract, 1.0-margin/2)
ppgplot.pgswin(0.0, 1.0, 0.0, 1.0)
ppgplot.pgtext(0.1, 0.8, "Frac Recovered" % frp)
ppgplot.pgtext(0.2, 0.65, "Power = %.3f" % frp)
ppgplot.pgtext(0.1, 0.4, "signal z = %.1f" % z)
ppgplot.pgtext(0.1, 0.25, "signal w = %.1f" % w)
# freq cut
ppgplot.pgsvp(margin, margin+imfract, margin+imfract, 1.0-margin/2)
ppgplot.pgswin(min(rs), max(rs), -0.1, 1.1)
ppgplot.pgbox("BCST", 0.0, 0, "BCNST", 0.0, 0)
temperature = 20 # Temperature in keV
log_lambda = 1 # Optical depth parameter
geometry = 0 # Mysterious geometry parameter
inclination =81 # Inclination of orbit
phi = 20 # Angle offset of magnetic axis to orbital axis (not used yet)
phaseArray = []
angleArray = []
# Plot theta as a function of orbital phase for these parameters
for phase in numpy.arange(0.5, 1.51, 0.01):
theta = computeViewingAngle(phase, inclination, beta, phi)
phaseArray.append(phase)
angleArray.append(theta)
mainPlotWindow = ppgplot.pgopen("/xs")
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(min(phaseArray), max(phaseArray), 0, 180, 0, 0)
ppgplot.pgline(phaseArray, angleArray)
ppgplot.pgsls(2)
ppgplot.pgline([0.5, 1.5], [90, 90])
ppgplot.pglab("orbital phase", "viewing angle", "Viewing angle \gh as a function of orbital phase.")
ppgplot.pgtext(0.6, 150, "i:%2.0f \gb:%2.0f \gf:%2.0f"%(inclination, beta, phi))
modelPlotWindow = ppgplot.pgopen("models_i_81_b_40.ps/ps")
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgask(False)
mainFluxMax = 0
#! /usr/bin/env python
#
# pgex1: freely taken after PGDEMO1.F
#
import ppgplot, numpy
import sys
# create an array
xs = numpy.array([1., 2., 3., 4., 5.])
ys = numpy.array([1., 4., 9., 16., 25.])
# creat another array
yr = 0.1 * numpy.array(range(0, 60))
xr = yr * yr
# pgplotting
if len(sys.argv) > 1: # if we got an argument use the argument as devicename
ppgplot.pgopen(sys.argv[1])
else:
ppgplot.pgopen('/xwin')
ppgplot.pgenv(0., 10., 0., 20., 0, 1)
ppgplot.pglab('(x)', '(y)', 'PGPLOT Example 1: y = x\u2')
ppgplot.pgpt(xs, ys, 9)
ppgplot.pgline(xr, yr)
ppgplot.pgclos()
temperature = 20 # Temperature in keV
log_lambda = 1 # Optical depth parameter
geometry = 0 # Mysterious geometry parameter
inclination = 81 # Inclination of orbit
phi = 20 # Angle offset of magnetic axis to orbital axis (not used yet)
phaseArray = []
angleArray = []
# Plot theta as a function of orbital phase for these parameters
for phase in numpy.arange(0.5, 1.51, 0.01):
theta = computeViewingAngle(phase, inclination, beta, phi)
phaseArray.append(phase)
angleArray.append(theta)
mainPlotWindow = ppgplot.pgopen("/xs")
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(min(phaseArray), max(phaseArray), 0, 180, 0, 0)
ppgplot.pgline(phaseArray, angleArray)
ppgplot.pgsls(2)
ppgplot.pgline([0.5, 1.5], [90, 90])
ppgplot.pglab("orbital phase", "viewing angle",
"Viewing angle \gh as a function of orbital phase.")
ppgplot.pgtext(0.6, 150,
"i:%2.0f \gb:%2.0f \gf:%2.0f" % (inclination, beta, phi))
modelPlotWindow = ppgplot.pgopen("models_i_81_b_40.ps/ps")
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgask(False)
step = period * 60. / numPoints
times = []
brightness = []
for x in range(numPoints):
times.append(seconds)
brightness.append(sinecurve(seconds, period))
seconds+= step
lightCurve = lightCurve()
lightCurve.initValues(times, brightness)
lightCurve.period = period * 60.
lightCurve.brightness[3] = 1.0
lightCurvePlot = {}
lightCurvePlot['pgplotHandle'] = ppgplot.pgopen('/xs')
ppgplot.pgpap(8, 0.618)
ppgplot.pgenv(0., lightCurve.period, 0.0, 1.0, 0, 0)
ppgplot.pglab("seconds", "brightness", "Light curve")
ppgplot.pgpt(lightCurve.time, lightCurve.brightness, 2)
ppgplot.pgsci(2)
ppgplot.pgline(lightCurve.time, lightCurve.brightness)
ppgplot.pgask(False)
connectedCount = 0
connectedBulbs = []
for b in bulbs:
print b
if b['connected'] == True:
connectedCount+= 1
connectedBulbs.append(b)
rgx = num.asarray([rs[0], rs[np - 1]])
rgy = num.asarray([zs[0], zs[np - 1]])
freqcut = pffdot[np / 2, :]
fdotcut = pffdot[:, np / 2]
image = 'antirainbow'
device = 'ffdot_combined.eps/VCPS'
device = '/XWIN'
labx = 'Fourier Frequency Offset (bins)'
laby = 'Fourier Frequency Derivative (bins)'
contours = num.asarray([0.1, 0.3, 0.5, 0.7, 0.9])
imfract = 0.65
margin = 0.08
ppgplot.pgopen(device)
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgpage()
# Give z and w values and power change
ppgplot.pgsvp(margin + imfract, 1.0 - margin / 2, margin + imfract,
1.0 - margin / 2)
ppgplot.pgswin(0.0, 1.0, 0.0, 1.0)
ppgplot.pgtext(0.1, 0.8, "Frac Recovered" % frp)
ppgplot.pgtext(0.2, 0.65, "Power = %.3f" % frp)
ppgplot.pgtext(0.1, 0.4, "signal z = %.1f" % z)
ppgplot.pgtext(0.1, 0.25, "signal w = %.1f" % w)
# freq cut
ppgplot.pgsvp(margin, margin + imfract, margin + imfract, 1.0 - margin / 2)
ppgplot.pgswin(min(rs), max(rs), -0.1, 1.1)
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()
#! /usr/bin/env python
#
# pgex1: freely taken after PGDEMO1.F
#
import ppgplot, numpy
import sys
# create an array
xs=numpy.array([1.,2.,3.,4.,5.])
ys=numpy.array([1.,4.,9.,16.,25.])
# creat another array
yr = 0.1*numpy.array(range(0,60))
xr = yr*yr
# pgplotting
if len(sys.argv) > 1: # if we got an argument use the argument as devicename
ppgplot.pgopen(sys.argv[1])
else:
ppgplot.pgopen('/xwin')
ppgplot.pgenv(0.,10.,0.,20.,0,1)
ppgplot.pglab('(x)', '(y)', 'PGPLOT Example 1: y = x\u2')
ppgplot.pgpt(xs,ys,9)
ppgplot.pgline(xr,yr)
ppgplot.pgclos()
def prepplot(rangex, rangey, title=None, labx=None, laby=None, \
rangex2=None, rangey2=None, labx2=None, laby2=None, \
logx=0, logy=0, logx2=0, logy2=0, font=ppgplot_font_, \
fontsize=ppgplot_font_size_, id=0, aspect=1, ticks='in', \
panels=[1,1], device=ppgplot_device_):
"""
prepplot(rangex, rangey, ...)
Open a PGPLOT device for plotting.
'rangex' and 'rangey' are sequence objects giving min and
max values for each axis.
The optional entries are:
title: graph title (default = None)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: ranges for 2nd x-axis (default = None)
rangey2: ranges for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: Show ID line on plot (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
global ppgplot_dev_open_, ppgplot_dev_prep_
# Check if we will use second X or Y axes
# Note: if using a 2nd X axis, the range should correspond
# to the minimum and maximum values of the 1st X axis. If
# using a 2nd Y axis, the range should correspond to the
# scalerange() values of the 1st Y axis.
if rangex2 is None:
rangex2 = rangex
otherxaxis = 0
else:
otherxaxis = 1
if rangey2 is None:
rangey2 = rangey
otheryaxis = 0
else:
otheryaxis = 1
# Open the plot device
if (not ppgplot_dev_open_):
ppgplot.pgopen(device)
# Let the routines know that we already have a device open
ppgplot_dev_open_ = 1
# Set the aspect ratio
ppgplot.pgpap(0.0, aspect)
if (panels != [1, 1]):
# Set the number of panels
ppgplot.pgsubp(panels[0], panels[1])
ppgplot.pgpage()
# Choose the font
ppgplot.pgscf(font)
# Choose the font size
ppgplot.pgsch(fontsize)
# Choose the font size
ppgplot.pgslw(ppgplot_linewidth_)
# Plot the 2nd axis if needed first
if otherxaxis or otheryaxis:
ppgplot.pgvstd()
ppgplot.pgswin(rangex2[0], rangex2[1], rangey2[0], rangey2[1])
# Decide how the axes will be drawn
if ticks == 'in': env = "CMST"
else: env = "CMSTI"
if logx2: lxenv = 'L'
else: lxenv = ''
if logy2: lyenv = 'L'
else: lyenv = ''
if otherxaxis and otheryaxis:
ppgplot.pgbox(env + lxenv, 0.0, 0, env + lyenv, 0.0, 0)
elif otheryaxis:
ppgplot.pgbox("", 0.0, 0, env + lyenv, 0.0, 0)
else:
ppgplot.pgbox(env + lxenv, 0.0, 0, "", 0.0, 0)
# Now setup the primary axis
ppgplot.pgvstd()
ppgplot.pgswin(rangex[0], rangex[1], rangey[0], rangey[1])
# Decide how the axes will be drawn
if ticks == 'in': env = "ST"
else: env = "STI"
if logx: lxenv = 'L'
else: lxenv = ''
if logy: lyenv = 'L'
else: lyenv = ''
if otherxaxis and otheryaxis:
ppgplot.pgbox("BN" + env + lxenv, 0.0, 0, "BN" + env + lyenv, 0.0, 0)
elif otheryaxis:
ppgplot.pgbox("BCN" + env + lxenv, 0.0, 0, "BN" + env + lyenv, 0.0, 0)
elif otherxaxis:
ppgplot.pgbox("BN" + env + lxenv, 0.0, 0, "BCN" + env + lyenv, 0.0, 0)
else:
ppgplot.pgbox("BCN" + env + lxenv, 0.0, 0, "BCN" + env + lyenv, 0.0, 0)
# Add labels
if not title is None: ppgplot.pgmtxt("T", 3.2, 0.5, 0.5, title)
ppgplot.pgmtxt("B", 3.0, 0.5, 0.5, labx)
ppgplot.pgmtxt("L", 2.6, 0.5, 0.5, laby)
if otherxaxis: ppgplot.pgmtxt("T", 2.0, 0.5, 0.5, labx2)
if otheryaxis: ppgplot.pgmtxt("R", 3.0, 0.5, 0.5, laby2)
# Add ID line if required
if (id == 1): ppgplot.pgiden()
# Let the routines know that we have already prepped the device
ppgplot_dev_prep_ = 1
filenames = [] # Load the first set of spectra. filename = arg.files fileList = open(filename, 'r') for line in fileList: filenames.append(str(line.strip())) for fileIndex, f in enumerate(filenames): spectrum = spectrumClasses.spectrumObject() spectrum.loadFromJSON(f) print "Loaded %s, contains %s."%(f, spectrum.objectName) spectra.append(spectrum) mainPGPlotWindow = ppgplot.pgopen(device) ppgplot.pgask(True) pgPlotTransform = [0, 1, 0, 0, 0, 1] 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.pgline(spectrum.wavelengths, spectrum.flux)
if (arg.usefirstframe):
window.setData(image)
bkg_sigma = 1.48 * mad(image)
sources = daofind(image, fwhm=4.0, threshold=3*bkg_sigma)
window.setSourcesAvoidBorders(sources)
else:
window.setBlankData(image)
allWindows.append(window)
(xmin, ymin, xmax, ymax) = determineFullFrameSize(allWindows)
fullFramexsize = xmax - xmin
fullFrameysize = ymax - ymin
lightcurveView = ppgplot.pgopen('/xs')
ppgplot.pgenv(startFrame, startFrame + frameRange, 0, 100, 0, 0)
ppgplot.pgask(False)
if (arg.preview):
bitmapView = ppgplot.pgopen('/xs')
ppgplot.pgenv(0.,fullFramexsize,0.,fullFrameysize, 1, 0)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsfs(2)
xValues = []
yValues = []
yAxisMax= 100
referenceApertures = ultraspecClasses.referenceApertures()
referenceApertures.initFromSourceList(sourceList)
def plot(self, vlo=2., vhi=98., nc=-1, method='p', mpl=False, cmap=CMDEF, \
close=True, x1=None, x2=None, y1=None, y2=None, sepmin=1.):
"""
Plots an MCCD using pgplot or matplotlib if preferred.
:Parameters:
vlo : float
number specifying the lowest level to plot (default as a percentile)
vhi : float
number specifying the lowest level to plot (default as a percentile)
nc : int
CCD number (starting from 0, -1 for all)
method : string
how vlo and vhi are to be interpreted. 'p' = percentile, 'a' = automatic (min to max,
vlo and vhi are irrelevant), 'd' = direct, i.e. just take the values given.
mpl : bool
True to prefer matplotlib over pgplot (which may not even be an option)
cmap : matplotlib.cm.binary
colour map if using matplotlib
close : bool
close (pgplot) or 'show' (matplotlib) the plot at the end (or not, to allow
you to plot something else, use a cursor etc). In the case of pgplot, this also
implies opening the plot at the start, i.e. a self-contained quick plot.
x1 : float
left-hand plot limit. Defaults to 0.5
x2 : float
right-hand plot limit. Defaults to nxmax+0.5
y1 : float
lower plot limit. Defaults to 0.5
y2 : float
upper plot limit. Defaults to nymax+0.5
sepmin : float
minimum separation between intensity limits (> 0 to stop PGPLOT complaining)
:Returns:
range(s) : tuple or list
the plot range(s) used either as a single 2-element tuple, or
a list of them, one per CCD plotted.
"""
if nc == -1:
nc1 = 0
nc2 = len(self)
else:
nc1 = nc
nc2 = nc+1
if not mpl:
if close: pg.pgopen('/xs')
if nc2-nc1 > 1: pg.pgsubp(nc2-nc1,1)
prange = []
for nc, ccd in enumerate(self._data[nc1:nc2]):
# Determine intensity range to display
if method == 'p':
vmin, vmax = ccd.centile((vlo,vhi))
elif method == 'a':
vmin, vmax = ccd.min(), ccd.max()
elif method == 'd':
vmin, vmax = vlo, vhi
else:
raise UltracamError('MCCD.plot: method must be one of p, a or d.')
if vmin == vmax:
vmin -= sepmin/2.
vmax += sepmin/2.
prange.append((vmin, vmax))
# start
nxmax, nymax = ccd.nxmax, ccd.nymax
x1 = 0.5 if x1 is None else x1
x2 = nxmax+0.5 if x2 is None else x2
y1 = 0.5 if y1 is None else y1
y2 = nymax+0.5 if y2 is None else y2
if mpl:
if nc2-nc1 > 1:
plt.subplot(1,nc2-nc1,nc+1)
plt.axis('equal')
else:
if nc2-nc1 > 1: pg.pgpanl(nc-nc1+1,1)
pg.pgwnad(x1,x2,y1,y2)
# plot CCD
ccd.plot(vmin,vmax,mpl,cmap)
# per-frame finishing-off
if mpl:
plt.xlim(x1,x2)
plt.ylim(y1,y2)
else:
pg.pgbox('bcnst',0,0,'bcnst',0,0)
pg.pglab('X','Y','')
if close:
if mpl:
plt.show()
else:
pg.pgclos()
# return intensity range(s) used
if len(prange) == 1:
return prange[0]
else:
return tuple(prange)
imageData = hdulist[1].data
savedImageData = numpy.copy(imageData)
wcsSolution = WCS(hdulist[1].header)
hdulist.close()
(height, width) = numpy.shape(imageData)
aspectRatio = float(height) / float(width)
print (aspectRatio)
""" Set up the PGPLOT windows """
imagePlot = {}
imagePlot["pgplotHandle"] = ppgplot.pgopen("/xs")
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
# ppgplot.pgenv(0., width,0., height, 1, -2)
imagePlot["pgPlotTransform"] = [0, 1, 0, 0, 0, 1]
boostedImage = generalUtils.percentiles(imageData, 20, 99)
ppgplot.pggray(boostedImage, 0, width - 1, 0, height - 1, 0, 255, imagePlot["pgPlotTransform"])
# Determine the RA, DEC of the centre of the image, using the WCS solution found in the FITS header
imageCentre = [width / 2, height / 2]
ra, dec = wcsSolution.all_pix2world([imageCentre], 1)[0]
else:
print '# Warning: no sources found to match ', src
os._exit(1)
if _do_grace:
# -- switch lists to arrays (should have done that to begin with) for pgplot
t = Numeric.array(date) # time (in days)
t = t - t0 # but relative to first date found
f = Numeric.array(flux) # flux
e = 3 * Numeric.array(ferr) # flux errors as 3 sigma
g = Numeric.array(freq) # freq
p = gracePlot.gracePlot()
p.plot(t, f, e, symbols=1)
p.title('Fluxes for ' + src)
p.xlabel('Days since ' + d0)
p.ylabel('Flux (Jy)')
for r in [[70, 100], [100, 120], [120, 250]]:
n, x, y, dy = get_range(t, f, e, g, r[0], r[1], cut)
if n: p.plot(x, y, dy, symbols=1)
p.hold(1)
elif _do_pgplot:
t = Numeric.array(date) # time (in days)
t = t - t0 # but relative to first date found
f = Numeric.array(flux) # flux
ppgplot.pgopen(pgp)
ppgplot.pgenv(0, 1500, 0, 80, 0, 1)
ppgplot.pglab('Days since %s' % d0, 'Flux(Jy)', 'Flux for %s' % src)
ppgplot.pgpt(t, f, 9)
ppgplot.pgline(t, f)
referenceSpectrum.objectName = "M-dwarf spectrum"
tioSpectrum = copy.deepcopy(referenceSpectrum)
tioSpectrum.trimWavelengthRange(7000, 7500)
tioArea = tioSpectrum.integrate()
print "tioArea", tioArea
tio1Range = (7540, 7580)
tio2Range = (7675, 7715)
tio1reference = referenceSpectrum.integrate(wavelengthrange = tio1Range)
tio2reference = referenceSpectrum.integrate(wavelengthrange = tio2Range)
tioRatio = tio1reference / tio2reference
tioDifference = tio1reference - tio2reference
print "tio1reference, tio2reference:", tio1reference, tio2reference, tioRatio, tioDifference
mainPGPlotWindow = ppgplot.pgopen('/xs')
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yUpper = 2.5
yLower = -0.5
newSpectra = []
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, yLower, yUpper, 0, 0)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
if hasEphemeris:
l, b = w.wcs_pix2world(np.stack((x, y), axis=-1), 1).T
else:
l, b = w.wcs_pix2world([[x, y]], 1)[0]
return l, b
if __name__ == "__main__":
# Load stars
s = Stars()
# Load the master class
m = Skymap()
# Initialize plot
ppgplot.pgopen("/xs")
ppgplot.pgslw(2)
ppgplot.pgpap(0.0, 0.75)
# For ever loop
redraw = True
while True:
# Redraw
if redraw == True:
# Update
m.update()
# Initialize window
ppgplot.pgscr(0, 0., 0., 0.)
ppgplot.pgeras()
ppgplot.pgsvp(0.01, 0.99, 0.01, 0.99)
def plot(command, data, group, cdata):
"""
Plots dsets from a dictionary called data.
Interactive usage:
plot slots [device x1 x2 y1 y2 xoff ysep=0 pmask]
Arguments:
slots -- range of slots as in '1-10', or just a single slot '9' to plot, or a group
name such as 'ippeg'.
device -- plot device (e.g. '/xs', '3/xs', 'hardcopy.ps/cps')
x1 -- left-hand plot limit
x2 -- right-hand plot limit. Set = x1 for automatic determination
y1 -- lower plot limit
y2 -- upper plot limit. Set = y1 for automatic determination
xoff -- offset to start X axis from, 0 for automatic determination.
ysep -- separation in y
pmask -- whether to plot masked data
"""
# generate arguments
inpt = inp.Input(DINT_ENV, DINT_DEF, inp.clist(command))
# register parameters
inpt.register('slots', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('device', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('xoff', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('ysep', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('pmask', inp.Input.LOCAL, inp.Input.HIDE)
# get inputs
slots = inpt.get_value('slots', 'slots to plot', '1')
slist = interp_slots(slots, True, data, group)
device = inpt.get_value('device', 'plot device', '/xs')
x1 = inpt.get_value('x1', 'left-hand plot limit', 0.0)
x2 = inpt.get_value('x2', 'right-hand plot limit', 0.0)
y1 = inpt.get_value('y1', 'lower plot limit', 0.0)
y2 = inpt.get_value('y2', 'upper plot limit', 0.0)
xoff = inpt.get_value('xoff', 'X offset', 0.0)
inpt.set_default('ysep', 0.0)
ysep = inpt.get_value('ysep', 'vertical separation between successive dsets', 0.0)
pmask = inpt.get_value('pmask', 'do you want to plot the masked data too?', True)
# Determine limits automatically if required
if xoff !=0. or x1 == x2 or y1 == y2:
xa1 = None
xa2 = None
ya1 = None
ya2 = None
yadd = 0.
for i in slist:
(xi1,xi2,yi1,yi2) = data[i].plimits()
if xa1 is None:
xa1 = xi1
else:
xa1 = min(xa1, xi1)
if xa2 is None:
xa2 = xi2
else:
xa2 = max(xa2, xi2)
if ya1 is None and yi1 is not None:
ya1 = yi1 + yadd
elif yi1 is not None:
ya1 = min(ya1, yi1 + yadd)
if ya2 is None and yi2 is not None:
ya2 = yi2 + yadd
elif yi2 is not None:
ya2 = max(ya2, yi2 + yadd)
yadd += ysep
if xa1 is None or xa2 is None or ya1 is None or ya2 is None:
raise DintError('plot: no automatic limits could be evaluated; possibly no good data to plot?')
if xoff == 0.0 and (xa2 - xa1) < (xa1+xa2)/2./100.:
xoff = xa1
xa1 -= xoff
xa2 -= xoff
if x1 == x2:
x1 = xa1
x2 = xa2
if y1 == y2:
y1 = ya1
y2 = ya2
try:
pg.pgopen(device)
pg.pgsci(4)
pg.pgenv(x1, x2, y1, y2, 0, 0)
pg.pgsci(2)
first = data[slist[0]]
xlabel = first.x.label if xoff == 0 else first.x.label + '-' + str(xoff)
xlabel += ' (' + first.x.units + ')'
ylabel = first.y.label + ' (' + first.y.units + ')'
pg.pglab(xlabel, ylabel, first.title)
yadd = 0.
for slot in slist:
data[slot].plot(xoff,yoff=-yadd,masked=pmask)
if not cdata.mute:
print('Plotted slot ' + str(slot))
yadd += ysep
pg.pgclos()
except pg.ioerror, err:
raise DintError(str(err))
referenceSpectrum = spectra[0]
normalConstant = referenceSpectrum.integrate((arg.nl, arg.nu))
print "Normalisation constant:", normalConstant
for index in range(1, len(spectra)):
s = spectra[index]
normalVal = s.integrate((arg.nl, arg.nu))
print "Normalisation value:", normalVal, normalConstant
spectra[index].divide(normalVal/normalConstant)
normalVal = s.integrate((arg.nl, arg.nu))
print "New Normalisation value:", normalVal, normalConstant
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:
def ok():
pg.pgopen('/null')
self.ccd.plot(5,95)
pg.pgclos()
return True
numSources = len(allSources)
maxSources = int(round((numSources)*0.4))
debug.write("Number of sources: %d, number of top sources: %d"%(numSources, maxSources), 2)
if maxSources<1:
debug.write("WARNING: Not enough sources for shift calculation, proceeding in '--noshift' mode.", 1)
applyShift = False
else:
topSources = allSources[0:maxSources]
masterApertureList = [ (x, y) for (x, y, flux) in topSources]
# Plot the preview frame
if (arg.keyimages):
#stackedFigure = matplotlib.pyplot.figure(figsize=(10, 10))
#matplotlib.pyplot.title("Initial 10 frame stacked image")
stackedPreview = ppgplot.pgopen('/xs')
ppgplot.pgenv(0.,fullFramexsize,0.,fullFrameysize, 1, 0)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pglab("x", "y", "Initial 10 frame stacked image.")
# Display the image on the user's screen
image = matplotlib.pyplot.imshow(boostedFullFrame, cmap='gray_r')
for s in allSources:
x, y = s[0], s[1]
matplotlib.pyplot.gca().add_artist(matplotlib.pyplot.Circle((x,y), 10, color='green', fill=False, linewidth=1.0))
if applyShift:
for s in topSources:
x, y = s[0], s[1]
matplotlib.pyplot.gca().add_artist(matplotlib.pyplot.Circle((x,y), 10, color='blue', fill=False, linewidth=1.0))
rows, cols = numpy.shape(boostedFullFrame)
yield f[0, ...]
def sum_it_impl(ar, quant):
# shape = (nheap, nspec, nchan)
# we sum over nspec
a2 = numpy.sum(quant(ar), axis=1)
# now shape = (nheap, nchan)
# we reorganize as:
# (nheap, 1, nchan)
a2.shape = ((a2.shape[0], 1, a2.shape[1]))
print "a2's shape = ", a2.shape
return a2
sum_it = lambda quant: (lambda ar: sum_it_impl(ar, quant))
#proc_file = lambda quant: lambda fn: map(draw_one, rdspec(fn, quant))
#proc_file = lambda quant: lambda fn: map(draw_one, rdbeamform(fn, quant))
#proc_file = lambda quant: lambda fn: map(draw_one, rdbeamform_nt(fn, quant))
proc_file = lambda quant: lambda fn: map(draw_one, rdbeamform_hv(fn, quant))
#proc_file = lambda quant: lambda fn: map(draw_one, rdbeamform_hv_avg(fn, quant))
#ppgplot.pgopen("42/xw")
#ppgplot.pgopen("/tmp/amplitude-1spectrum.ps/cps")
#ppgplot.pgopen("/tmp/amplitude-1spectrum.png/png")
ppgplot.pgopen("?")
ppgplot.pgask(False)
#map(proc_file(numpy.abs), sys.argv[1:])
map(proc_file(sum_it(numpy.abs)), sys.argv[1:])
#map(proc_file(lambda x: numpy.angle(x, True)), sys.argv[1:])
def setmask(command, data, cdata):
"""
Sets the mask on a dset and dumps a file containing the mask which can be applied
to other dsets using 'appmask'. This is an interactive routine which will request
input from the user and is better not used in batch processing. Repeated calls of
this routine can be used to build complex masks. The masks are always applied in
the original order so that you can mask then partially unmask for example. Note that
whatever slot you choose to define the mask will always end up masked; if you don't
want this you may want to make a copy.
Interactive usage:
setmask slot mfile append [device reset x1 x2 y1 y2] mask type
Arguments:
slot -- an example slot to plot.
mfile -- mask file
append -- append to an old mask file if possible
device -- plot device, e.g. '/xs'
reset -- rest plot limits or not
x1 -- left X plot limit
x2 -- right X plot limit
y1 -- bottom Y plot limit
y2 -- top Y plot limit
mask -- mask 'M', or unmask 'U' or quit 'Q'.
type -- type of mask: 'X' masks using ranges in X
Mask types:
X -- mask a range in X
Y -- mask a range in Y
I -- mask a range of pixel indices
P -- mask in 'phase', i.e. a range that repeats periodically.
"""
import trm.dnl.mask as mask
# generate arguments
inpt = inp.Input(DINT_ENV, DINT_DEF, inp.clist(command))
# register parameters
inpt.register('slot', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('mfile', inp.Input.GLOBAL, inp.Input.PROMPT)
inpt.register('append', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('device', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('reset', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('mask', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('type', inp.Input.LOCAL, inp.Input.PROMPT)
# get inputs
slots = inpt.get_value('slot', 'slot to plot for mask definition', '1')
slist = interp_slots(slots, True, data, nfind=1)
dset = data[slist[0]]
device = inpt.get_value('device', 'plot device', '/xs')
# mask file
mfile = inpt.get_value('mfile', 'mask file to save results to', subs.Fname('mask','.msk', subs.Fname.NEW))
append = inpt.get_value('append', 'add to an old mask file if possible', True)
if append and mfile.exists():
mptr = open(mfile,'rb')
gmask = pickle.load(mptr)
gmask.app_mask(dset)
mptr.close()
else:
gmask = mask.Gmask()
# other parameters
reset = inpt.get_value('reset', 'reset plot limits automatically?', True)
# compute default limits
(x1,x2,y1,y2) = dset.plimits()
if (x2 - x1) < (x1+x2)/2./100.:
xoff = x1
x1 = 0.
x2 -= xoff
else:
xoff = 0.
yoff = 0.
if reset:
inpt.set_default('x1', x1)
inpt.set_default('x2', x2)
inpt.set_default('y1', y1)
inpt.set_default('y2', y2)
x1 = inpt.get_value('x1', 'left-hand limit of plot', x1)
x2 = inpt.get_value('x2', 'right-hand limit of plot', x2)
y1 = inpt.get_value('y1', 'bottom limit of plot', y1)
y2 = inpt.get_value('y2', 'top limit of plot', y2)
m_or_u = inpt.get_value('mask', 'M(ask), U(nmask) or Q(uit)?', 'm', lvals=['m', 'M', 'u', 'U', 'q', 'Q'])
if m_or_u.upper() == 'M':
mtext = 'mask'
else:
mtext = 'unmask'
mask_type = inpt.get_value('type', 'X, Y, P(hase), I(ndex) or Q(uit)?', 'x',
lvals=['x', 'X', 'y', 'Y', 'p', 'P', 'i', 'I', 'q', 'Q'])
# initialise plot
try:
pg.pgopen(device)
pg.pgsch(1.5)
pg.pgscf(2)
pg.pgslw(2)
pg.pgsci(4)
pg.pgenv(x1,x2,y1,y2,0,0)
(xlabel,ylabel) = dset.plabel(xoff,yoff)
pg.pgsci(2)
pg.pglab(xlabel, ylabel, dset.title)
# plot the dset
dset.plot(xoff,yoff)
x = (x1+x2)/2.
y = (y1+y2)/2.
# now define masks
ch = 'X'
while ch.upper() != 'Q':
# go through mask options
if mask_type.upper() == 'X':
print('Set cursor at the one end of the X range, Q to quit')
(xm1,y,ch) = pg.pgband(7,0,x,y)
if ch.upper() != 'Q':
print('Set cursor at the other end of ' +
'the X range, Q to quit')
xm2,y,ch = pg.pgband(7,0,xm1,y)
if ch.upper() != 'Q':
if xm1 > xm2: xm1,xm2 = xm2,xm1
umask = mask.Xmask(xoff+xm1, xoff+xm2, m_or_u.upper() == 'M')
elif mask_type.upper() == 'I':
print('Place cursor near a point and click to ' +
mtext + ' it, Q to quit')
x,y,ch = pg.pgband(7,0,x,y)
if ch.upper() != 'Q':
xmm1,xmm2,ymm1,ymm2 = pg.pgqvp(2)
xscale = (xmm2-xmm1)/(x2-x1)
yscale = (ymm2-ymm1)/(y2-y1)
# only consider good data of opposite 'polarity' to the
# change we are making.
ok = (dset.good == True) & \
(dset.mask == (m_or_u.upper() == 'M'))
if len(dset.x.dat[ok == True]):
# compute physical squared distance of cursor from
# points
sqdist = npy.power(
xscale*(dset.x.dat[ok]-(xoff+x)),2) + \
npy.power(yscale*(dset.y.dat[ok]-(yoff+y)),2)
# select the index giving the minimum distance
indices = npy.arange(len(dset))[ok]
index = indices[sqdist.min() == sqdist][0]
umask = mask.Imask(index, m_or_u.upper() == 'M')
else:
print('There seem to be no data to ' + mtext +
'; data already ' + mtext + 'ed are ignored.')
umask = None
if ch.upper() != 'Q' and umask is not None:
gmask.append(umask)
umask.app_mask(dset)
print('overplotting data')
# over-plot the dset
dset.plot(xoff,yoff)
pg.pgclos()
except pg.ioerror, err:
raise DintError(str(err))
print repr(card.header)
imageData = hdulist[0].data
wcsSolution = WCS(hdulist[0].header)
hdulist.close()
(height, width) = numpy.shape(imageData)
aspectRatio = float(height)/float(width)
print aspectRatio
""" Set up the PGPLOT windows """
imagePlot = {}
imagePlot['pgplotHandle'] = ppgplot.pgopen('/xs')
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
# ppgplot.pgenv(0., width,0., height, 1, -2)
imagePlot['pgPlotTransform'] = [0, 1, 0, 0, 0, 1]
boostedImage = generalUtils.percentiles(imageData, 20, 99)
ppgplot.pggray(boostedImage, 0, width-1, 0, height-1, 0, 255, imagePlot['pgPlotTransform'])
# Determine the RA, DEC of the centre of the image, using the WCS solution found in the FITS header
imageCentre = [ width/2, height/2]
ra, dec = wcsSolution.all_pix2world([imageCentre], 1)[0]
def prepplot(rangex, rangey, title=None, labx=None, laby=None, \
rangex2=None, rangey2=None, labx2=None, laby2=None, \
logx=0, logy=0, logx2=0, logy2=0, font=ppgplot_font_, \
fontsize=ppgplot_font_size_, id=0, aspect=1, ticks='in', \
panels=[1,1], device=ppgplot_device_):
"""
prepplot(rangex, rangey, ...)
Open a PGPLOT device for plotting.
'rangex' and 'rangey' are sequence objects giving min and
max values for each axis.
The optional entries are:
title: graph title (default = None)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: ranges for 2nd x-axis (default = None)
rangey2: ranges for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: Show ID line on plot (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
global ppgplot_dev_open_, ppgplot_dev_prep_
# Check if we will use second X or Y axes
# Note: if using a 2nd X axis, the range should correspond
# to the minimum and maximum values of the 1st X axis. If
# using a 2nd Y axis, the range should correspond to the
# scalerange() values of the 1st Y axis.
if rangex2 is None:
rangex2=rangex
otherxaxis=0
else: otherxaxis=1
if rangey2 is None:
rangey2=rangey
otheryaxis=0
else: otheryaxis=1
# Open the plot device
if (not ppgplot_dev_open_):
ppgplot.pgopen(device)
# My little add-on to switch the background to white
if device == '/XWIN':
reset_colors()
if device == '/AQT':
ppgplot.pgsci(0)
# Let the routines know that we already have a device open
ppgplot_dev_open_ = 1
# Set the aspect ratio
ppgplot.pgpap(0.0, aspect)
if (panels != [1,1]):
# Set the number of panels
ppgplot.pgsubp(panels[0], panels[1])
ppgplot.pgpage()
# Choose the font
ppgplot.pgscf(font)
# Choose the font size
ppgplot.pgsch(fontsize)
# Choose the font size
ppgplot.pgslw(ppgplot_linewidth_)
# Plot the 2nd axis if needed first
if otherxaxis or otheryaxis:
ppgplot.pgvstd()
ppgplot.pgswin(rangex2[0], rangex2[1], rangey2[0], rangey2[1])
# Decide how the axes will be drawn
if ticks=='in': env = "CMST"
else: env = "CMSTI"
if logx2: lxenv='L'
else: lxenv=''
if logy2: lyenv='L'
else: lyenv=''
if otherxaxis and otheryaxis:
ppgplot.pgbox(env+lxenv, 0.0, 0, env+lyenv, 0.0, 0)
elif otheryaxis:
ppgplot.pgbox("", 0.0, 0, env+lyenv, 0.0, 0)
else:
ppgplot.pgbox(env+lxenv, 0.0, 0, "", 0.0, 0)
# Now setup the primary axis
ppgplot.pgvstd()
ppgplot.pgswin(rangex[0], rangex[1], rangey[0], rangey[1])
# Decide how the axes will be drawn
if ticks=='in': env = "ST"
else: env = "STI"
if logx: lxenv='L'
else: lxenv=''
if logy: lyenv='L'
else: lyenv=''
if otherxaxis and otheryaxis:
ppgplot.pgbox("BN"+env+lxenv, 0.0, 0, "BN"+env+lyenv, 0.0, 0)
elif otheryaxis:
ppgplot.pgbox("BCN"+env+lxenv, 0.0, 0, "BN"+env+lyenv, 0.0, 0)
elif otherxaxis:
ppgplot.pgbox("BN"+env+lxenv, 0.0, 0, "BCN"+env+lyenv, 0.0, 0)
else:
ppgplot.pgbox("BCN"+env+lxenv, 0.0, 0, "BCN"+env+lyenv, 0.0, 0)
# My little add-on to switch the background to white
if device == '/AQT' or device == '/XWIN':
reset_colors()
# Add labels
if not title is None: ppgplot.pgmtxt("T", 3.2, 0.5, 0.5, title)
ppgplot.pgmtxt("B", 3.0, 0.5, 0.5, labx)
ppgplot.pgmtxt("L", 2.6, 0.5, 0.5, laby)
if otherxaxis: ppgplot.pgmtxt("T", 2.0, 0.5, 0.5, labx2)
if otheryaxis: ppgplot.pgmtxt("R", 3.0, 0.5, 0.5, laby2)
# Add ID line if required
if (id==1): ppgplot.pgiden()
# Let the routines know that we have already prepped the device
ppgplot_dev_prep_ = 1
#/usr/bin/env python
#
# pgex1: freely taken after PGDEMO1.F
#
import ppgplot, numpy
import sys
# create an array
xs=numpy.array([1.,2.,3.,4.,5.])
ys=numpy.array([1.,4.,9.,16.,25.])
# creat another array
yr = 0.1*numpy.array(range(0,60))
xr = yr*yr
# pgplotting
if len(sys.argv) > 1: # if we got an argument use the argument as devicename
ppgplot.pgopen(sys.argv[1])
else:
ppgplot.pgopen('?')
ppgplot.pgenv(0.,10.,0.,20.,0,1)
ppgplot.pglab('(x)', '(y)', 'PGPLOT Example 1: y = x\u2')
ppgplot.pgpt(xs,ys,9)
ppgplot.pgline(xr,yr)
ppgplot.pgclos()
sources = daofind(image, fwhm=4.0, threshold=3*bkg_sigma)
window.setSourcesAvoidBorders(sources)
else:
window.setBlankData(image)
allWindows.append(window)
(xmin, ymin, xmax, ymax) = determineFullFrameSize(allWindows)
fullFramexsize = xmax - xmin
fullFrameysize = ymax - ymin
""" Set up the PGPLOT windows """
xyPositionPlot = {}
xyPositionPlot['pgplotHandle'] = ppgplot.pgopen('/xs')
xyPositionPlot['yLimit'] = 1.0
xyPositionPlot['numXYPanels'] = len(referenceApertures.sources)
ppgplot.pgpap(6.18, 1.618)
ppgplot.pgsubp(1, xyPositionPlot['numXYPanels'])
ppgplot.pgsci(5)
for panel in range(xyPositionPlot['numXYPanels']):
currentSize = ppgplot.pgqch()
ppgplot.pgsch(1)
yLimit = xyPositionPlot['yLimit']
ppgplot.pgenv(startFrame, startFrame + frameRange, -yLimit, yLimit, 0, -2)
ppgplot.pgbox('A', 0.0, 0, 'BCG', 0.0, 0)
ppgplot.pglab("", "%d"%panel, "")
ppgplot.pgsch(currentSize)
ppgplot.pgask(False)
# /usr/bin/env python
#
# pgex1: freely taken after PGDEMO1.F
#
import ppgplot, Numeric
import sys
# create an array
xs = Numeric.array([1.0, 2.0, 3.0, 4.0, 5.0])
ys = Numeric.array([1.0, 4.0, 9.0, 16.0, 25.0])
# creat another array
yr = 0.1 * Numeric.array(range(0, 60))
xr = yr * yr
# pgplotting
if len(sys.argv) > 1: # if we got an argument use the argument as devicename
ppgplot.pgopen(sys.argv[1])
else:
ppgplot.pgopen("?")
ppgplot.pgenv(0.0, 10.0, 0.0, 20.0, 0, 1)
ppgplot.pglab("(x)", "(y)", "PGPLOT Example 1: y = x\u2")
ppgplot.pgpt(xs, ys, 9)
ppgplot.pgline(xr, yr)
ppgplot.pgclos()
