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 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 plotdVdz(): nv = 3. nr = 1. ppgplot.pgbeg("dVdz.ps/vcps", 1, 1) #color port. ppgplot.pgpap(8., 1.25) ppgplot.pgpage ppgplot.pgsch(1.2) #font size ppgplot.pgslw(3) #line width # 1st panel with symbols w/ stddev errorbars x1 = .15 x2 = .45 x3 = .6 x4 = .95 y1 = .15 y2 = .425 y3 = .575 y4 = .85 xlabel = 14.1 - 14. ylabel = 1.15 schdef = 1.2 slwdef = 4 ppgplot.pgsch(schdef) xmin = 0. xmax = 1.1 ymin = 0. ymax = 1.2 ppgplot.pgsvp(x1, x4, y1, y4) #sets viewport ppgplot.pgslw(slwdef) #line width ppgplot.pgswin(xmin, xmax, ymin, ymax) #axes limits ppgplot.pgbox('bcnst', .2, 2, 'bcvnst', .2, 2) #tickmarks and labeling ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "z") #xlabel ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, "(1/DH)\u3\d c dV\dc\u/dv/d\gW") z = N.arange(0., 5., .1) beta = ((1 + z)**2 - 1) / ((1 + z)**2 + 1) dV = N.zeros(len(z), 'd') for i in range(len(z)): #dz=dv/(1+z[i])*(1- ((1+z[i])**2 -1)/((1+z[i])**2+1))**(-2) #z1=z[i]-0.5*dz #z2=z[i]+0.5*dz #dV[i]=my.dL(z2,h) - my.dL(z1,h) dA = my.DA(z[i], h) * 206264. / 1000. dV[i] = DH * (1 + z[i]) * (dA)**2 / (my.E( z[i])) / (1 - beta[i])**2 / DH**3 #dV[i]=DH*(1+z[i])**2*(dA)**2/(my.E(z[i]))/DH**3#for comparison w/Hogg if z[i] < 1: print i, z[i], dV[i], dV[i]**(1. / 3.) ppgplot.pgline(z, dV) ppgplot.pgend()
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 plotngalsigmaradcuts(): nr = 1. nv = 3. bbJmax = -18. ppgplot.pgbeg("ngalmhalo-radcut.ps/vcps", 1, 1) #color port. ppgplot.pgpap(8., 1.25) ppgplot.pgpage ppgplot.pgsch(1.2) #font size ppgplot.pgslw(3) #line width # 1st panel with symbols w/ stddev errorbars str1 = "R\dp\u < " str2 = " R\dv\u" x1 = .1 x2 = .45 x3 = .6 x4 = .95 y1 = .15 y2 = .425 y3 = .575 y4 = .85 xlabel = 14.25 - 14. ylabel = 1.14 ppgplot.pgsvp(x1, x2, y3, y4) #sets viewport g.cutonlbj(bbJmax) #print "within plotradcuts, after cutonlbj, len(g.x1) = ",len(g.x1) nr = 1. c.measurengalcontam(nv, nr, g) #print "nr = ",nr, " ave contam = ",N.average(c.contam) sub1plotngalmcl(c.mass, c.membincut, c.obsmembincut) ppgplot.pgsch(.8) ppgplot.pgslw(3) #label="R\dp\u < "+str(nr)+"R\dv\u" label = str1 + str(nr) + str2 ppgplot.pgtext(xlabel, ylabel, label) nr = .5 ppgplot.pgsvp(x1, x2, y1, y2) #sets viewport #ppgplot.pgpanl(1,1) c.measurengalcontam(nv, nr, g) #print "nr = ",nr, " ave contam = ",N.average(c.contam) sub1plotngalmcl(c.mass, c.membincut, c.obsmembincut) label = str1 + str(nr) + str2 ppgplot.pgsch(.8) ppgplot.pgslw(3) ppgplot.pgtext(xlabel, ylabel, label) ppgplot.pgend()
def nextpage(vertical=False): """ Start a new page in the currently opened device. Default is to orient paper horizontally (landscape). If vertical is True then the paper will be oriented vertically. """ # New page if ppgplot.pgqid()!=0: ppgplot.pgpage() ppgplot.pgswin(0,1,0,1) if vertical: ppgplot.pgpap(7.9, 11.0/8.5) else: ppgplot.pgpap(10.25, 8.5/11.0) ppgplot.pgiden() else: sys.stderr.write("Cannot start new page. No pgplot device open.\n") raise "No open pgplot device"
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)
def beginplot(fn, vertical=False): """ Set up a colour ps plot with filename fn for letter-size paper. Default is to orient paper horizontally (landscape). If vertical is True then the paper will be oriented vertically. """ if ppgplot.pgqid()!=0: # ppgplot already has a device open # # QUESTION: Should raise error here? # pass else: # Setup ppgplot.pgbeg("%s/CPS" % fn, 1, 1) if vertical: ppgplot.pgpap(7.9, 11.0/8.5) else: ppgplot.pgpap(10.25, 8.5/11.0) ppgplot.pgiden()
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()
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) ppgplot.pgwnad(-1.5 * m.w, 1.5 * m.w, -m.w, m.w)
def joy_division_plot(pulses, timeseries, downfactor=1, hgt_mult=1): """Plot each pulse profile on the same plot separated slightly on the vertical axis. 'timeseries' is the Datfile object dissected. Downsample profiles by factor 'downfactor' before plotting. hgt_mult is a factor to stretch the height of the paper. """ first = True ppgplot.pgbeg("%s.joydiv.ps/CPS" % \ os.path.split(timeseries.basefn)[1], 1, 1) ppgplot.pgpap(10.25, hgt_mult*8.5/11.0) # Letter landscape # ppgplot.pgpap(7.5, 11.7/8.3) # A4 portrait, doesn't print properly ppgplot.pgiden() ppgplot.pgsci(1) # Set up main plot ppgplot.pgsvp(0.1, 0.9, 0.1, 0.8) ppgplot.pglab("Profile bin", "Single pulse profiles", "") to_plot = [] xmin = 0 xmax = None ymin = None ymax = None for pulse in pulses: vertical_offset = (pulse.number-1)*JOYDIV_SEP copy_of_pulse = pulse.make_copy() if downfactor > 1: # Interpolate before downsampling interp = ((copy_of_pulse.N/downfactor)+1)*downfactor copy_of_pulse.interpolate(interp) copy_of_pulse.downsample(downfactor) # copy_of_pulse.scale() if first: summed_prof = copy_of_pulse.profile.copy() first = False else: summed_prof += copy_of_pulse.profile prof = copy_of_pulse.profile + vertical_offset min = prof.min() if ymin is None or min < ymin: ymin = min max = prof.max() if ymax is None or max > ymax: ymax = max max = prof.size-1 if xmax is None or max > xmax: xmax = max to_plot.append(prof) yspace = 0.1*ymax ppgplot.pgswin(0, xmax, ymin-yspace, ymax+yspace) for prof in to_plot: ppgplot.pgline(np.arange(0,prof.size), prof) ppgplot.pgbox("BNTS", 0, 0, "BC", 0, 0) # Set up summed profile plot ppgplot.pgsvp(0.1, 0.9, 0.8, 0.9) ppgplot.pglab("", "Summed profile", "Pulses from %s" % timeseries.datfn) summed_prof = summed_prof - summed_prof.mean() ppgplot.pgswin(0, xmax, summed_prof.min(), summed_prof.max()) ppgplot.pgline(np.arange(0, summed_prof.size), summed_prof) ppgplot.pgbox("C", 0, 0, "BC", 0, 0) ppgplot.pgclos()
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()
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) print b['label'], "is connected!"
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) ppgplot.pgline(rs, freqcut)
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]
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", "lightcurve.ps/ps"] for plotDevice in plotDevices: mainPGPlotWindow = ppgplot.pgopen(plotDevice) pgPlotTransform = [0, 1, 0, 0, 0, 1] ppgplot.pgpap(arg.paper, 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) if not arg.invert:
ppgplot.pgenv(numpy.min(frequency) ,numpy.max(frequency) , numpy.min(power), numpy.max(power), 0, 0) ppgplot.pglab("Frequency (days\u-1\d)", "Power", "Periodogram: %s period: %f"%(o.id, period) ) ppgplot.pgline(frequency, power) print "Best frequency for %s is %f cycles/day or a period of %f days."%(o.id, bestFrequency, period) ppgplot.pgclos() ########################################################################################################################## # Phase Plots ########################################################################################################################## if arg.ps: device = "phaseplots.ps/ps" else: device = "/xs" phasePlotWindow = ppgplot.pgopen(device) pgPlotTransform = [0, 1, 0, 0, 0, 1] ppgplot.pgslct(phasePlotWindow) ppgplot.pgsci(1) ppgplot.pgpap(10, 0.618) ppgplot.pgask(True) for o in objects: if o.hasEphemeris: HJD = o.getColumn('HJD') mag = o.getColumn('mag') err = o.getColumn('err') phases = [o.ephemeris.getPhase(h) for h in HJD] extendPhases = copy.deepcopy(phases) for p in phases: extendPhases.append(p + 1.0) phases = extendPhases magMax = numpy.max(mag) + err[numpy.argmax(mag)] magMin = numpy.min(mag) - err[numpy.argmin(mag)] meanError = numpy.mean(err)
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 psplotinit(output): file=output+"/vcps" ppgplot.pgbeg(file,1,1) ppgplot.pgpap(8.,1.) ppgplot.pgsch(1.7) #font size ppgplot.pgslw(7) #line width
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) upperWavelength = max(spectrum.wavelengths) ppgplot.pgpap(6.18, 1.618) ppgplot.pgenv(lowerWavelength, upperWavelength, yLower, yUpper, 0, 0) for index, spectrum in enumerate(spectra): ppgplot.pgsci(1) flux = [ f + offset*index for f in spectrum.flux] ppgplot.pgbin(spectrum.wavelengths, flux) plotx = 8600 ploty = offset*index + spectrum.getNearestFlux(plotx) + offset/5 if hasEphemeris: ppgplot.pgptxt(plotx, ploty, 0, 0, "%1.2f"%(spectrum.phase)) if arg.title!=None: ppgplot.pglab("wavelength", "flux", arg.title) else: ppgplot.pglab("wavelength", "flux", spectrum.objectName)
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 mratiopg(): ppgplot.pgbeg("maccratio.ps/vcps",1,1) #color port. ppgplot.pgpap(8.,1.) ppgplot.pgpage ppgplot.pgsch(1.3) #font size ppgplot.pgslw(7) #line width # 1st panel with symbols w/ stddev errorbars #ylabel="SFR (M\d\(2281) \u yr\u-1\d)" ylabel="L(H\ga) (10\u41\d erg s\u-1\d)" xlabel="M\dr\u " x1=.15 x2=.5 x3=.5 x4=.85 y1=x1 y2=x2 y3=x3 y4=x4 emarker=18 smarker=23 xmin=N.log10(1.e14) xmax=N.log10(2.5e15) #ymin=-1. #ymax=3. ymin=0. ymax=25. ppgplot.pgsvp(x1,x4,y1,y4) #sets viewport ppgplot.pgswin(xmin,xmax,ymin,ymax) #axes limits ppgplot.pgbox('blncst',1.,2,'bcvnst',2.,2) #tickmarks and labeling for i in range(len(lz1lm.mass)): m=lz1lm.mass[i] l=lz1lm.maccret[i] h=hz1lm.maccret[i] r=h/l print i,m,l,h,r #print lz1lm.maccret #print hz1lm.maccret #print hz3lm.maccret r3lm=(hz3lm.maccret)/(lz3lm.maccret) r3hm=(hz3hm.maccret)/(lz3hm.maccret) #for i in range(len(r3)): # print i,lz3.sigma[i],hz3.sigma[i],lz3.mass[i],hz3.mass[i] # print i,lz01.sigma[i],hz01.sigma[i],lz01.mass[i],hz01.mass[i] r1lm=hz1lm.maccret/lz1lm.maccret r1hm=hz1hm.maccret/lz1hm.maccret #ra=N.array(hz01.maccret,'d') #rb=N.array(lz01.maccret,'d') #r01=ra/rb #for i in range(len(r01)): #print "ratio ",hz01.maccret[i],lz01.maccret[i],ra[i],rb[i],r01[i] ppgplot.pgsci(14) ppgplot.pgsls(1) ppgplot.pgline(N.log10(lz3lm.mass),r3lm) ppgplot.pgsls(2) ppgplot.pgline(N.log10(lz3hm.mass),r3hm) ppgplot.pgsci(1) ppgplot.pgsls(1) ppgplot.pgline(N.log10(lz1lm.mass),r1lm) ppgplot.pgsls(2) ppgplot.pgline(N.log10(lz1hm.mass),r1hm) xlabel='M\dcl\u (M\d\(2281)\u)' ylabel='M\dacc\u(z=0.75) / M\dacc\u(z=0.07)' ppgplot.pgsch(1.8) ppgplot.pgslw(7) ppgplot.pgmtxt('b',2.2,0.5,0.5,ylabel) #xlabel ppgplot.pgmtxt('l',2.5,0.5,0.5,xlabel) ppgplot.pgend()
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
# Also add the spectrum to the phase+1 bin phase = s.phase + 1.0 phaseBin = int(phase*float(numPhaseBins)) print phase, phaseBin existingSpectrum = trailArray[phaseBin] newSpectrum = (numpy.array(existingSpectrum) + numpy.array(s.flux)) / 2 trailArray[phaseBin] = newSpectrum trailBitmap = numpy.copy(trailArray) mainPGPlotWindow = ppgplot.pgopen(arg.device) ppgplot.pgask(True) ppgplot.pgpap(3.0, 1.8) # pgPlotTransform = [0, 1, 0, 0, 0, 1] spectrum = spectra[0] xScale = (max(spectrum.wavelengths) - min(spectrum.wavelengths)) / xSize if hasEphemeris: yScale = numPhaseBins else: yScale = len(spectra) pgPlotTransform = [min(spectrum.wavelengths), xScale, 0, 0, 0, 1/float(yScale)] lowerWavelength = min(spectrum.wavelengths) upperWavelength = max(spectrum.wavelengths) ppgplot.pgenv( min(spectrum.wavelengths), max(spectrum.wavelengths), 0, 2, 0, 0) ppgplot.pggray(generalUtils.percentiles(trailBitmap, 20, 99), 0, xSize-1 , 0, ySize-1 , 255, 0, pgPlotTransform) # ppgplot.pggray(trailBitmap, 0, xSize-1 , 0, ySize-1 , numpy.max(trailBitmap), numpy.min(trailBitmap), pgPlotTransform)
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
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)
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) ppgplot.pgsci(1)
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) ppgplot.pgenv(min(x_values), max(x_values), lowerY, upperY, 0, 0) ppgplot.pgslw(7)