def plotgal(xg,yg,final,finalsf,ncl):
ppgplot.pgslw(6)
ppgplot.pgsls(1)
xmin = (-1.*c.pscale[ncl]*(c.xc[ncl]-1))
xmax = (c.pscale[ncl]*(c.xmax[ncl]-c.xc[ncl]))
ymin = (-1.*c.pscale[ncl]*(c.yc[ncl]-1))
ymax = (c.pscale[ncl]*(c.ymax[ncl]-c.yc[ncl]))
ppgplot.pgbox("",0.0,0,"L",0.0,0)
dx=5.
ppgplot.pgenv(xmin-dx,xmax+dx,ymin-dx,ymax+dx,0)
ppgplot.pglab("\gD DEC (\")","\gD RA (\")","")
ppgplot.pgtext(-4,-4,"X")
r = (0.5*c.r200pix[ncl]*c.pscale[ncl])
ppgplot.pgslw(1)
ppgplot.pgsls(2)
ppgplot.pgsfs(2)
ppgplot.pgcirc(0,0,r)
#print "cluster ",ncl," r200: ",c.r200pix[ncl],c.r200Mpc[ncl], " Mpc"
ppgplot.pgslw(3)
ppgplot.pgsls(1)
x = (xg - c.xc[ncl])*c.pscale[ncl]
y = (yg - c.yc[ncl])*c.pscale[ncl]
x = (N.compress((final > 0) & (finalsf < 1), xg) - c.xc[ncl])*c.pscale[ncl]
y = (N.compress((final > 0) & (finalsf < 1), yg) - c.yc[ncl])*c.pscale[ncl]
ppgplot.pgpt(x,y,22)
x = (N.compress((final > 0) & (finalsf > 0), xg) - c.xc[ncl])*c.pscale[ncl]
y = (N.compress((final > 0) & (finalsf > 0), yg) - c.yc[ncl])*c.pscale[ncl]
ppgplot.pgpt(x,y,18)
def plotsigsff(sig, sf, file, nbin):
psplot = file + ".ps"
psplotinit(psplot)
tot = N.ones(len(sf), 'f')
(sigbin, sfbin) = my.binitsumequal(sig, sf, nbin)
(sigbin, totbin) = my.binitsumequal(sig, tot, nbin)
print sfbin
print totbin
(sff, sfferr) = my.ratioerror(sfbin, totbin)
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
ymin = -.05
ymax = 1.05
xmin = min(sig) - 10.
#xmax=max(sig)-200.
xmax = 350.
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0)
ppgplot.pglab("\gS\d5\u (gal/Mpc\u2\d)", "Fraction EW([OII])>4 \(2078)",
"")
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
sig = N.array(sig, 'f')
sff = N.array(sff, 'f')
ppgplot.pgsci(2)
ppgplot.pgline(sigbin, sff)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, sff, 17)
my.errory(sigbin, sff, sfferr)
ppgplot.pgend()
def xysimple(x, y, xlabel, ylabel):
xmax = max(x)
xmin = min(x)
ymax = max(y)
ymin = min(y)
ppgplot.pgbox("", 0.0, 0, "", 0.0, 0)
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0)
ppgplot.pglab(xlabel, ylabel, "")
ppgplot.pgpt(x, y, 3)
def plotold():
xmin=2.2
xmax=3.2
ymin=-2.5
ymax=-.5
psplotinit('fSsigma3Gyr.ps')
ppgplot.pgbox("",0.0,0,"",0.0,0)
ppgplot.pgenv(xmin,xmax,ymin,ymax,0,30)
ppgplot.pglab("\gs (km/s)",'fS(10\u11\d:10\u13\d)',"")
ppgplot.pgsci(1)
ppgplot.pgline(sigma,frac)
ppgplot.pgsls(2)
ppgplot.pgsci(2)
ppgplot.pgline(sigma08,frac08)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
ppgplot.pgend()
xmin=2.2
xmax=3.2
ymin=11.
ymax=14.2
psplotinit('maccretsigma3Gyr.ps')
ppgplot.pgbox("",0.0,0,"",0.0,0)
ppgplot.pgenv(xmin,xmax,ymin,ymax,0,30)
ppgplot.pglab("\gs (km/s)",'M\dacc\u (M\d\(2281)\u)',"")
ppgplot.pgsci(1)
ppgplot.pgline(sigma,maccret)
ppgplot.pgsls(2)
ppgplot.pgsci(2)
ppgplot.pgline(sigma08,maccret08)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
mylines=N.arange(-20.,20.,.4)
mylineswidth=3
ppgplot.pgsls(4)
ppgplot.pgslw(mylineswidth)
x=N.arange(0.,5.,1.)
lines=mylines
for y0 in lines:
y=3*x +y0
ppgplot.pgline(x,y)
ppgplot.pgsls(1)
ppgplot.pgend()
os.system('cp maccretsigma.ps /Users/rfinn/SDSS/paper/.')
os.system('cp fSsigma.ps /Users/rfinn/SDSS/paper/.')
def makeplot():
psplotinit("noise.ps")
DATAMIN = 0.
DATAMAX = 15.
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
#print "making graph, ncl = ",ncl
path = os.getcwd()
f = path.split('/')
#print path
#print f
prefix = f[4]
title = prefix
ymin = -.05
ymax = max(aveaperr) + .1
#ymax=10.
ppgplot.pgenv(DATAMIN, DATAMAX, ymin, ymax, 0)
ppgplot.pglab("linear size N of aperture (pixel)", "rms in Sky (ADU/s)",
title)
ppgplot.pgsci(2) #red
ppgplot.pgslw(4) #line width
x = N.sqrt(avearea)
y = aveaperr
ppgplot.pgpt(x, y, 7)
#errory(x,y,erry)
ppgplot.pgsci(1) #black
#ppgplot.pgpt(isoarea,fluxerriso,3)
#x1=N.sqrt(contsubisoarea)
#y1=contsuberr
#x1=N.sqrt(isoarea)
#y1=fluxerriso
#y=n*y1
#ppgplot.pgpt(x1,y1,1)
#ppgplot.pgsci(4)#blue
#ppgplot.pgpt(x1,y,1)
#ppgplot.pgsci(1)#black
x = N.arange(0, 50, 1)
y = x * (a + b * a * x)
#y=N.sqrt(x)*.02
ppgplot.pgline(x, y)
#errory(x,y,erry)
ppgplot.pgend()
def plotsig10sffall(sigspec, sigphot, sf, file, nbin):
psplot = file + ".ps"
psplotinit(psplot)
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
ymin = -.01
ymax = 1.01
#xmin=min(sigspec)-10.
#xmax=max(sig)-200.
#xmax=400.
xmin = -1.
xmax = 2.7
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0, 10)
ppgplot.pglab("\gS\d10\u (gal/Mpc\u2\d)", "Fraction EW([OII])>4 \(2078)",
"")
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
tot = N.ones(len(sf), 'f')
(sigbin, sfbin) = my.binitsumequal(sigspec, sf, nbin)
(sigbin, totbin) = my.binitsumequal(sigspec, tot, nbin)
(sff, sfferr) = my.ratioerror(sfbin, totbin)
#sig=N.array(sig,'f')
#sff=N.array(sff,'f')
ppgplot.pgsci(2)
sigbin = N.log10(sigbin)
ppgplot.pgline(sigbin, sff)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, sff, 17)
my.errory(sigbin, sff, sfferr)
(sigbin, sfbin) = my.binitsumequal(sigphot, sf, nbin)
(sigbin, totbin) = my.binitsumequal(sigphot, tot, nbin)
(sff, sfferr) = my.ratioerror(sfbin, totbin)
#sig=N.array(sig,'f')
#sff=N.array(sff,'f')
ppgplot.pgslw(4) #line width
ppgplot.pgsci(4)
sigbin = N.log10(sigbin)
ppgplot.pgline(sigbin, sff)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, sff, 21)
#my.errory(sigbin,sff,sfferr)
ppgplot.pgend()
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 compdiff(x, y, xlabel, ylabel):
xmax = max(x)
xmin = min(x)
ymax = max(y)
ymin = min(y)
ave = N.average(N.compress((x < 21) & (im1.sn > 3), y))
std = scipy.stats.std(N.compress((x < 21) & (im1.sn > 3), y))
ppgplot.pgbox("", 0.0, 0, "", 0.0, 0)
ppgplot.pgenv(xmin, xmax, -.8, .8, 0)
ppgplot.pglab(xlabel, ylabel, "")
ppgplot.pgpt(x, y, 3)
x = N.arange((int(xmin) - 1), (int(xmax) + 2), 1)
y = ave * x / x
ppgplot.pgsci(2)
ppgplot.pgline(x, y)
ppgplot.pgsci(1)
y1 = y - std
ppgplot.pgline(x, y1)
y1 = y + std
ppgplot.pgline(x, y1)
def plotoutlierpos():
dm = 0.01
xlabel = "x position of outliers"
ylabel = "y position of outliers"
delta = abs(im1.magap5 - im2.magap5)
x = N.compress(delta > dm, im1.x)
y = N.compress(delta > dm, im1.y)
xmax = max(x)
xmin = min(x)
ymax = max(y)
ymin = min(y)
ppgplot.pgbox("", 0.0, 0, "", 0.0, 0)
#ppgplot.pgenv(xmin,xmax,ymin,ymax,0)
ppgplot.pgenv(0, 920, 0, 875, 0)
ppgplot.pglab(xlabel, ylabel, "")
ppgplot.pgpt(x, y, 3)
x = N.compress(delta > dm, im2.x)
y = N.compress(delta > dm, im2.y)
diff = N.compress(delta > dm, delta)
ppgplot.pgpt(x, y, 4)
for i in range(len(x)):
print x[i], y[i], diff[i]
def getChiSqByParameters(params, *args):
global iteration, mainPlotWindow, currentPlotWindow, colour, inclination, phase
print "Params:", params
beta = params[0]
log_lambda = params[1]
scale_factor = params[2]
linear_offset = params[3]
print "Args:", args
temperature = args[0]
field = args[1]
# cos(theta) = cos(i)cos(beta) - sin(i)sin(beta)cos(phi + pi/2)
cosTheta = math.cos(radians(inclination)) * math.cos(radians(beta)) - math.sin(radians(inclination)) * math.sin(radians(beta))*math.cos(phase + math.pi/2.)
angle = math.acos(cosTheta) / math.pi * 180
print "Angle: %f [deg], Field: %f [MG], Temperature:%f [keV], log_lambda: %f, scale: %f, offset: %f"%(angle, field, temperature, log_lambda, scale_factor, linear_offset)
model = getSampledModel(observedSpectrum.wavelengths, angle, field, temperature, log_lambda)
model = [m * scale_factor + linear_offset for m in model]
chi = computeChiSq(observedSpectrum, model)
allChiSqs.append(chi)
print "Chi-squared:", chi
startWavelength = min(observedSpectrum.wavelengths)
endWavelength = max(observedSpectrum.wavelengths)
# Draw the most recent iteration
ppgplot.pgslct(currentPlotWindow)
ppgplot.pgsci(1)
ppgplot.pgenv(startWavelength, endWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgline(observedSpectrum.wavelengths, observedSpectrum.flux)
ppgplot.pgsci(4)
ppgplot.pgline(observedSpectrum.wavelengths, model)
ppgplot.pgsci(1)
ppgplot.pglab("wavelength", "flux", "Current fit: %d"%iteration)
# Overplot the iteration on the original diagram
print "overplotting"
ppgplot.pgslct(mainPlotWindow)
ppgplot.pgsci(colour)
ppgplot.pgline(observedSpectrum.wavelengths, model)
colour += 1
if colour>15: colour = 1
ppgplot.pgsci(1)
# Re-generate the Chi-Squared plot
ppgplot.pgslct(chiSqPlotWindow)
if iteration > 9:
ppgplot.pgenv(0, iteration+1, 0, max(allChiSqs), 0, 0)
else:
ppgplot.pgenv(0, 10, 0, max(allChiSqs), 0, 0)
iterations = range(iteration+1)
ppgplot.pgpt(iterations, allChiSqs, 2)
minCh = min(allChiSqs)
medCh = numpy.median(allChiSqs)
maxCh = max(allChiSqs)
ppgplot.pglab("Iteration [n]", "Chi-squared", "Chi-squared values [%.2f, %.2f, %.2f]"%(minCh, medCh, maxCh))
iteration += 1
return chi
def plotsigo2(sig, o2, file, nbin):
psplot = file + ".ps"
psplotinit(psplot)
(sigbin, o2bin) = my.binit(sig, o2, nbin)
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
ymin = -10.
ymax = 2.
xmin = min(sig) - 10.
#xmax=max(sig)-200.
xmax = 350.
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0)
ppgplot.pglab("\gS\d5\u (gal/Mpc\u2\d)", "EW([OII]) (\(2078))", "")
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
sig = N.array(sig, 'f')
o2 = N.array(o2, 'f')
ppgplot.pgpt(sig, o2, 1)
ppgplot.pgsci(2)
ppgplot.pgline(sigbin, o2bin)
ppgplot.pgsci(1)
ppgplot.pgend()
print upper, MJD, expectedTime, phaseDifference, ominusc, ominusc*86400., error, terror, sigma, ocerror
ocs.append(ominusc*86400.)
cycles.append(upper)
ocErrors.append(ocerror)
for c, o, oe in zip(cycles, ocs, ocErrors):
print c, o, oe
plotname = "QSVir-oc"
plotDevices = ["/xs", "%s.eps/ps"%plotname]
for plotDevice in plotDevices:
mainPGPlotWindow = ppgplot.pgopen(plotDevice)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgpap(10, 0.618)
ppgplot.pgsci(1)
ppgplot.pgenv(min(cycles)*1.05, max(cycles)*1.05, min(ocs)*1.15, max(ocs)*1.15, 0, 0)
ppgplot.pgslw(1)
ppgplot.pgpt(cycles, ocs, 2)
ppgplot.pgslw(1)
ppgplot.pgerrb(2, cycles, ocs, ocErrors, 0)
ppgplot.pgerrb(4, cycles, ocs, ocErrors, 0)
ppgplot.pgsls(2)
ppgplot.pglab("Cycle number", "O-C (seconds)", "")
ppgplot.pgclos()
""""# Map some colours
oColours = []
for o in observatories:
index = o % len(colourMap)
colour = colourMap[index]
oColours.append(colour)
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)
for frameIndex in range(2, frameRange + 1):
# 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)
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(currentPlotWindow)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 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()
fig = matplotlib.pyplot.gcf() fig.suptitle(runStr + ' ' + fileappendix, fontsize=20) matplotlib.pyplot.draw() matplotlib.pyplot.show(block = False) fig.savefig(runStr +'_%s.eps'%fileappendix,dpi=100, format='eps') fig.savefig(runStr +'_%s.png'%fileappendix,dpi=100, format='png') plotDevices = ["/xs", "%s.eps/ps"%fileappendix] for plotDevice in plotDevices: mainPGPlotWindow = ppgplot.pgopen(plotDevice) pgPlotTransform = [0, 1, 0, 0, 0, 1] ppgplot.pgpap(10, 0.618) ppgplot.pgsci(1) ppgplot.pgenv(min(x_values), max(x_values), yLims[0], yLims[1], 0, 0) ppgplot.pgslw(7) ppgplot.pgpt(x_values, y_values, 1) ppgplot.pgslw(1) ppgplot.pgerrb(2, x_values, y_values, y_errors, 0) ppgplot.pgerrb(4, x_values, y_values, y_errors, 0) ppgplot.pgsls(2) ppgplot.pgline(xFit, yFit) ppgplot.pgsls(3) ppgplot.pgline([a3, a3], [yLims[0], yLims[1]]) ppgplot.pgsls(1) ppgplot.pglab(xColumn + " - " + str(JDoffset), "flux ratio", "") ppgplot.pgclos() time.sleep(3) times = []
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
mainFluxMin = 1E99
for phase in numpy.arange(0.5, 1.6, 0.1):
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)
ppgplot.pggray(boostedFullFrame, 0, cols-1, 0, rows-1, 0, 255, pgPlotTransform)
n = 1.3
if ncl == 5:
title = "MS1054"
#a=.008
#b=.165*a
n = 1.45
if ncl == 6:
title = "HDFN1"
#a=.008
#b=.165*a
n = 1.45
ymin = -.05
ymax = max(aveaperr)
#ymax=10.
ppgplot.pgenv(DATAMIN, DATAMAX, ymin, ymax, 0)
ppgplot.pglab("linear size N of aperture (pixel)", "rms in Sky (ADU/s)", title)
ppgplot.pgsci(2) #red
ppgplot.pgslw(4) #line width
x = N.sqrt(avearea)
y = aveaperr
ppgplot.pgpt(x, y, 7)
#errory(x,y,erry)
ppgplot.pgsci(1) #black
#ppgplot.pgpt(isoarea,fluxerriso,3)
#x1=N.sqrt(contsubisoarea)
#y1=contsuberr
x1 = N.sqrt(isoarea)
y1 = fluxerriso
y = n * y1
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)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
if hasEphemeris:
phase = ephemeris.getPhase(spectrum.HJD)
labelX = 8100
labelY = spectrum.getNearestFlux(labelX) + 0.5
print labelX, labelY
ppgplot.pglab("Wavelength", "Flux", "Phase: %f"%phase)
# Now remove the 8190 doublet from the data set
continuum = copy.deepcopy(spectrum)
continuum.snipWavelengthRange(8130, 8250)
ppgplot.pgsci(3)
ppgplot.pgline(continuum.wavelengths, continuum.flux)
x_values = continuum.wavelengths
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))
for frameIndex, image in enumerate(images):
redData, greenData, blueData = image.split()
# redData.show()
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):
MJD = o.getColumn('MJD')
mag = o.getColumn('mag')
err = o.getColumn('err')
startDate = numpy.min(MJD)
endDate = numpy.max(MJD)
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)
ppgplot.pgenv(startDate, endDate, magMax + meanError*2, magMin - meanError*2, 0, 0)
ppgplot.pgpt(MJD, mag)
ppgplot.pgerrb(2, MJD, mag, err, 0)
ppgplot.pgerrb(4, MJD, mag, err, 0)
ppgplot.pglab("MJD", "CRTS mag", "%s [%d]"%(o.id, len(MJD)))
ppgplot.pgclos()
# Compute HJDs for the observations
for o in objects:
hasEphemeris = o.loadEphemeris()
if hasEphemeris: o.computeHJDs()
def gotoit():
nbin = 10
#c=Cluster()
#g=Galaxy()
clusterfile = "clusters.spec.dat"
print "reading in cluster file to get cluster parameters"
c.creadfiles(clusterfile)
print "got ", len(c.z), " clusters"
c.convarray()
c.Kcorr()
go2 = [] #combined arrays containing all galaxies
gsf = [] #combined arrays containing all galaxies
gsig5 = []
gsig10 = []
gsig52r200 = [] #spec catalogs extended out to 2xR200
gsig102r200 = [] #spec catalogs extended out to 2xR200
gsig5phot = []
gsig10phot = []
sgo2 = [] #combined arrays containing all galaxies
sgha = [] #combined arrays containing all galaxies
sgsf = [] #combined arrays containing all galaxies
sgsig5 = []
sgsig10 = []
sgsig52r200 = [] #spec catalogs extended out to 2xR200
sgsig102r200 = [] #spec catalogs extended out to 2xR200
sgsig5phot = []
sgsig10phot = []
if (mode < 1):
c.getsdssphotcats()
c.getsdssspeccats()
gr = [] #list of median g-r colors
psplotinit('summary.ps')
x1 = .1
x2 = .45
x3 = .6
x4 = .95
y1 = .15
y2 = .45
y3 = .55
y4 = .85
ppgplot.pgsch(1.2) #font size
ppgplot.pgslw(2)
#for i in range(len(c.z)):
cl = [10]
(xl, xu, yl, yu) = ppgplot.pgqvp(0)
print "viewport = ", xl, xu, yl, yu
complall = []
for i in range(len(c.z)):
#for i in cl:
gname = "g" + str(i)
gname = Galaxy()
gspecfile = "abell" + str(c.id[i]) + ".spec.dat"
gname.greadfiles(gspecfile, i)
print "number of members = ", len(gname.z)
if len(gname.z) < 10:
print "less than 10 members", len(gname.z)
continue
gname.convarray()
#gname.cullmembers()
#gname.getmemb()#get members w/in R200
#gr.append(N.average(gname.g-gname.r))
gspec2r200file = "abell" + str(c.id[i]) + ".spec2r200.dat"
gname.greadspecfiles(gspec2r200file, c.dL[i], c.kcorr[i], i)
print i, c.id[i], " getnearest, first call", len(gname.ra), len(
gname.sra), sum(gname.smemb)
#gname.getnearest(i)
(gname.sig52r200, gname.sig102r200) = gname.getnearestgen(
gname.ra, gname.dec, gname.sra, gname.sdec, i
) #measure distances from ra1, dec1 to members in catalog ra2, dec2
sig52r200 = N.compress(gname.memb > 0, gname.sig52r200)
gsig52r200[len(gsig5phot):] = sig52r200
sig102r200 = N.compress(gname.memb > 0, gname.sig102r200)
gsig102r200[len(gsig10phot):] = sig102r200
gphotfile = "abell" + str(c.id[i]) + ".phot.dat"
gname.greadphotfiles(gphotfile, c.dL[i], c.kcorr[i])
gname.getnearest(i)
#print "len of local density arrays = ",len(gname.sig5),len(gname.sig5phot)
#print gspecfile, c.z[i],c.kcorr[i]
(ds5, ds10) = gname.gwritefiles(gspecfile, i)
o2 = N.compress(gname.memb > 0, gname.o2)
go2[len(go2):] = o2
sf = N.compress(gname.memb > 0, gname.sf)
gsf[len(gsf):] = sf
sig5 = N.compress(gname.memb > 0, gname.sig5)
gsig5[len(gsig5):] = sig5
sig10 = N.compress(gname.memb > 0, gname.sig10)
gsig10[len(gsig10):] = sig10
sig5phot = N.compress(gname.memb > 0, gname.sig5phot)
gsig5phot[len(gsig5phot):] = sig5phot
sig10phot = N.compress(gname.memb > 0, gname.sig10phot)
gsig10phot[len(gsig10phot):] = sig10phot
ds5 = N.array(ds5, 'f')
ds10 = N.array(ds10, 'f')
#print len(ds5),len(ds10)
#ppgplot.pgsvp(xl,xu,yl,yu)
ppgplot.pgsvp(0.1, .9, .08, .92)
ppgplot.pgslw(7)
label = 'Abell ' + str(
c.id[i]) + ' (z=%5.2f, \gs=%3.0f km/s)' % (c.z[i], c.sigma[i])
ppgplot.pgtext(0., 1., label)
ppgplot.pgslw(2)
ppgplot.pgsvp(x1, x2, y1, y2) #sets viewport
#ppgplot.pgbox("",0.0,0,"",0.0)
ppgplot.pgswin(-1., 3., -1., 3.) #axes limits
ppgplot.pgbox('bcnst', 1, 2, 'bcvnst', 1, 2) #tickmarks and labeling
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5,
"\gS\d10\u(phot) (gal/Mpc\u2\d)") #xlabel
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, "\gS\d10\u(spec) (gal/Mpc\u2\d)")
x = N.arange(-5., 10., .1)
y = x
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgline(x, y)
x = N.log10(sig10phot)
y = N.log10(sig10)
ppgplot.pgsch(.7)
ppgplot.pgpt(x, y, 17)
xp = N.array([-0.5], 'f')
yp = N.array([2.5], 'f')
ppgplot.pgpt(xp, yp, 17)
ppgplot.pgtext((xp + .1), yp, 'spec(1.2xR200) vs phot')
ppgplot.pgsci(4)
xp = N.array([-0.5], 'f')
yp = N.array([2.2], 'f')
ppgplot.pgpt(xp, yp, 21)
ppgplot.pgtext((xp + .1), yp, 'spec(2xR200) vs phot')
y = N.log10(sig102r200)
ppgplot.pgsch(.9)
ppgplot.pgpt(x, y, 21)
ppgplot.pgsch(1.2)
ppgplot.pgslw(2) #line width
ppgplot.pgsci(1)
#ppgplot.pgenv(-200.,200.,-1.,20.,0,0)
#ppgplot.pgsci(2)
#ppgplot.pghist(len(ds5),ds5,-200.,200.,30,1)
#ppgplot.pgsci(4)
#ppgplot.pghist(len(ds10),ds10,-200.,200.,30,1)
#ppgplot.pgsci(1)
#ppgplot.pglab("\gD\gS","Ngal",gspecfile)
#ppgplot.pgpanl(1,2)
g = N.compress(gname.memb > 0, gname.g)
r = N.compress(gname.memb > 0, gname.r)
V = N.compress(gname.memb > 0, gname.V)
dmag = N.compress(gname.memb > 0, gname.dmagnearest)
dnearest = N.compress(gname.memb > 0, gname.nearest)
dz = N.compress(gname.memb > 0, gname.dz)
#ppgplot.pgsvp(x3,x4,y1,y2) #sets viewport
#ppgplot.pgenv(-.5,3.,-1.,5.,0,0)
#ppgplot.pgpt((g-V),(g-r),17)
#ppgplot.pgsci(1)
#ppgplot.pglab("g - M\dV\u",'g-r',gspecfile)
ppgplot.pgsvp(x1, x2, y3, y4) #sets viewport
#ppgplot.pgbox("",0.0,0,"",0.0)
ppgplot.pgswin(
(c.ra[i] + 2. * c.r200deg[i] / N.cos(c.dec[i] * N.pi / 180.)),
(c.ra[i] - 2 * c.r200deg[i] / N.cos(c.dec[i] * N.pi / 180.)),
(c.dec[i] - 2. * c.r200deg[i]), (c.dec[i] + 2. * c.r200deg[i]))
ppgplot.pgbox('bcnst', 0.0, 0.0, 'bcvnst', 0.0,
0.0) #tickmarks and labeling
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "RA") #xlabel
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, "Dec")
#ppgplot.pglab("RA",'Dec',gspecfile)
ppgplot.pgsfs(2)
ppgplot.pgcirc(c.ra[i], c.dec[i], c.r200deg[i])
ppgplot.pgsls(4)
ppgplot.pgcirc(c.ra[i], c.dec[i], 1.2 * c.r200deg[i])
ppgplot.pgsls(1)
#ppgplot.pgcirc(c.ra[i],c.dec[i],c.r200deg[i]/N.cos(c.dec[i]*N.pi/180.))
ppgplot.pgsci(2)
ppgplot.pgpt(gname.ra, gname.dec, 17)
ppgplot.pgsci(4)
ppgplot.pgpt(gname.photra, gname.photdec, 21)
ppgplot.pgsci(1)
#calculate completeness w/in R200
dspec = N.sqrt((gname.ra - c.ra[i])**2 + (gname.dec - c.dec[i])**2)
dphot = N.sqrt((gname.photra - c.ra[i])**2 +
(gname.photdec - c.dec[i])**2)
nphot = 1. * len(N.compress(dphot < c.r200deg[i], dphot))
nspec = 1. * len(N.compress(dspec < c.r200deg[i], dspec))
s = "Completeness for cluster Abell %s = %6.2f (nspec=%6.1f,nphot= %6.1f)" % (
str(c.id[i]), float(nspec / nphot), nspec, nphot)
print s
complall.append(float(nspec / nphot))
ppgplot.pgsvp(x3, x4, y3, y4) #sets viewport
#ppgplot.pgsvp(x1,x2,y3,y4) #sets viewport
#ppgplot.pgbox("",0.0,0,"",0.0)
ppgplot.pgswin(-0.005, .05, -1., 1.)
ppgplot.pgbox('bcnst', .02, 2, 'bcvnst', 1, 4) #tickmarks and labeling
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5,
"Dist to nearest phot neighbor (deg)") #xlabel
ppgplot.pgsch(1.2)
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'M\dV\u(phot) - M\dV\u(spec)')
ppgplot.pgsci(2)
ppgplot.pgpt(dnearest, dmag, 17)
ppgplot.pgsci(1)
x = N.arange(-30., 30., 1.)
y = 0 * x
ppgplot.pgsci(1)
ppgplot.pgsls(2)
ppgplot.pgline(x, y)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
dm = N.compress(dnearest < 0.01, dmag)
std = '%5.3f (%5.3f)' % (pylab.mean(dm), pylab.std(dm))
#ppgplot.pgslw(7)
#label='Abell '+str(c.id[i])
#ppgplot.pgtext(0.,1.,label)
ppgplot.pgslw(2)
label = '\gDM\dV\u(err) = ' + std
ppgplot.pgsch(.9)
ppgplot.pgtext(0., .8, label)
#label = "z = %5.2f"%(c.z[i])
#ppgplot.pgtext(0.,.8,label)
ppgplot.pgsch(1.2)
#ppgplot.pgsvp(x3,x4,y3,y4) #sets viewport
#ppgplot.pgenv(-.15,.15,-3.,3.,0,0)
#ppgplot.pgsci(2)
#ppgplot.pgpt(dz,dmag,17)
#ppgplot.pgsci(1)
#ppgplot.pglab("z-z\dcl\u",'\gD Mag',gspecfile)
ppgplot.pgsvp(x3, x4, y1, y2) #sets viewport
ppgplot.pgswin(-3., 3., -1., 1.)
ppgplot.pgbox('bcnst', 1, 2, 'bcvnst', 1, 4) #tickmarks and labeling
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "\gDv/\gs") #xlabel
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'M\dV\u(phot) - M\dV\u(spec)')
ppgplot.pgsci(2)
dv = dz / (1 + c.z[i]) * 3.e5 / c.sigma[i]
ppgplot.pgpt(dv, dmag, 17)
ppgplot.pgsci(1)
x = N.arange(-30., 30., 1.)
y = 0 * x
ppgplot.pgsci(1)
ppgplot.pgsls(2)
ppgplot.pgline(x, y)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
#ppgplot.pgsvp(x1,x2,y1,y2) #sets viewport
#ppgplot.pgenv(0.,3.5,-3.,3.,0,0)
#ppgplot.pgsci(4)
#ppgplot.pgpt((g-r),dmag,17)
#ppgplot.pgsci(1)
#ppgplot.pglab("g-r",'\gD Mag',gspecfile)
#ppgplot.pgsvp(x1,x2,y1,y2) #sets viewport
#ppgplot.pgenv(-25.,-18.,-1.,1.,0,0)
#ppgplot.pgsci(4)
#ppgplot.pgpt((V),dmag,17)
#x=N.arange(-30.,30.,1.)
#y=0*x
#ppgplot.pgsci(1)
#ppgplot.pgsls(2)
#ppgplot.pgline(x,y)
#ppgplot.pgsls(1)
#ppgplot.pgsci(1)
#ppgplot.pglab("M\dV\u(spec)",'M\dV\u(phot) - M\dV\u(spec)',gspecfile)
#ppgplot.pgpage()
#ppgplot.pgpage()
#combine galaxy data
ppgplot.pgpage()
(sssig5,
sssig10) = gname.getnearestgen(gname.sra, gname.sdec, gname.sra,
gname.sdec,
i) #get spec-spec local density
(spsig5,
spsig10) = gname.getnearestgen(gname.sra, gname.sdec, gname.photra,
gname.photdec,
i) #get spec-phot local density
o2 = N.compress(gname.smemb > 0, gname.so2)
sgo2[len(sgo2):] = o2
ha = N.compress(gname.smemb > 0, gname.sha)
sgha[len(sgha):] = ha
sf = N.compress(gname.smemb > 0, gname.ssf)
sgsf[len(sgsf):] = sf
sig5 = N.compress(gname.smemb > 0, sssig5)
sgsig5[len(sgsig5):] = sig5
sig10 = N.compress(gname.smemb > 0, sssig10)
sgsig10[len(sgsig10):] = sig10
sig5phot = N.compress(gname.smemb > 0, spsig5)
sgsig5phot[len(sgsig5phot):] = sig5phot
sig10phot = N.compress(gname.smemb > 0, spsig10)
sgsig10phot[len(sgsig10phot):] = sig10phot
#gr=N.array(gr,'f')
#c.assigncolor(gr)
#for i in range(len(c.z)):
# print c.id[i],c.z[i],c.r200[i],c.r200deg[i]
print "Average Completeness w/in R200 = ", N.average(N.array(
complall, 'f'))
print "sig o2", len(gsig10), len(gsig10phot), len(go2)
print "sig o2 large", len(sgsig10), len(sgsig10phot), len(sgo2)
plotsigo2all(gsig10, gsig10phot, go2, 'o2vsig10spec', nbin)
#plotsigo2(gsig5phot,-1*go2,'o2vsig5phot',nbin)
plotsigsff(gsig5, gsf, 'sffvsig5spec', nbin) #sf frac versus sigma
plotsigsff(gsig5phot, gsf, 'sffvsig5phot', nbin) #sf frac versus sigma
plotsigsffall(gsig5, gsig5phot, gsf, 'sffvsig5all',
nbin) #sf frac versus sigma
plotsig10sffall(gsig10, gsig10phot, gsf, 'sffvsig10all',
nbin) #sf frac versus sigma
#plotsighaall(gsig10,gsig10phot,gha,'havsig10spec',20)
#plotsigo2all(sgsig10,sgsig10phot,sgo2,'o2vsig10spec.large',30)
plotsighaall(sgsig10, sgsig10phot, sgha, 'havsig10spec.large', 10)
#plotsigsffall(sgsig5,sgsig5phot,sgsf,'sffvsig5.large',nbin)#sf frac versus sigma
#plotsig10sffall(sgsig10,sgsig10phot,sgsf,'sffvsig10.large',nbin)#sf frac versus sigma
psplotinit('one2one.ps')
ppgplot.pgenv(-1.5, 2.5, -1.5, 2.5, 0)
ppgplot.pglab("\gS\d10\u(phot) (gal/Mpc\u2\d)",
"\gS\d10\u(spec) (gal/Mpc\u2\d)", "")
x = N.arange(-5., 10., .1)
y = x
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgline(x, y)
x = N.log10(gsig10phot)
y = N.log10(gsig10)
ppgplot.pgsch(.7)
ppgplot.pgpt(x, y, 17)
ppgplot.pgsch(1.)
ppgplot.pgsci(1)
ppgplot.pgend()
def draw_one(x):
ppgplot.pgbbuf()
ppgplot.pgenv(0, len(x), numpy.min(x) * 0.95, numpy.max(x) * 1.05)
ppgplot.pgpt(numpy.arange(len(x)), x)
ppgplot.pgebuf()
time.sleep(4)
# /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()
if not arg.stacked:
mainPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yUpper = 2.5
yLower = -0.5
for spectrum in spectra:
ppgplot.pgsci(1)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgbin(spectrum.wavelengths, spectrum.flux)
if hasEphemeris:
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%f]"%(spectrum.objectName, spectrum.phase))
else:
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%s]"%(spectrum.objectName, spectrum.loadedFromFilename))
if arg.stacked:
mainPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yLower = -0.5
offset = 2.0
yUpper = numSpectra * offset
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)
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))
#/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()
i_s = []
for line in inputfile:
if line[0] == '#':
continue
data = line.split()
replaceExpChar(data[0])
freqs.append(float(replaceExpChar(data[0])))
i_0s.append(float(replaceExpChar(data[1])))
i_1s.append(float(replaceExpChar(data[2])))
i_s.append(float(replaceExpChar(data[3])))
lowerFreq = min(freqs)
upperFreq = max(freqs)
upper_i0 = max(i_0s)
lower_i0 = max(i_0s)
upper_i1 = max(i_1s)
lower_i1 = min(i_1s)
upper_i = max(i_s)
lower_i = min(i_s)
mainPGPlotWindow = ppgplot.pgopen('/xs')
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgenv(lowerFreq, upperFreq, lower_i, upper_i, 0, 0)
ppgplot.pgsci(1)
ppgplot.pgline(freqs, i_s)
ppgplot.pglab("frequency", "i_", "")
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)
epochs = [s.HJD for s in spectra]
startHJD = min(epochs)
endHJD = max(epochs)
if arg.title is None:
title = ""
else:
title = arg.title
if hasEphemeris:
# ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "Phase", "%s - %s"%(str(startHJD), str(endHJD)))
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "Phase", title)
else:
#ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "Spectrum number", "%s - %s"%(str(spectra[0].HJD), str(spectra[-1].HJD)))
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):
MJD = o.getColumn('MJD')
mag = o.getColumn('mag')
err = o.getColumn('err')
startDate = numpy.min(MJD)
endDate = numpy.max(MJD)
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)
ppgplot.pgenv(startDate, endDate, magMax + meanError*2, magMin - meanError*2, 0, 0)
ppgplot.pgpt(MJD, mag)
ppgplot.pgerrb(2, MJD, mag, err, 0)
ppgplot.pgerrb(4, MJD, mag, err, 0)
ppgplot.pglab("MJD", "CRTS mag", "%s [%d]"%(o.id, len(MJD)))
ppgplot.pgclos()
# Compute HJDs for the observations
for o in objects:
hasEphemeris = o.loadEphemeris()
if hasEphemeris: o.computeHJDs()
# Load the PTF data
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)
ppgplot.pgenv(0, numpy.max(dates), magMax + meanError*2, magMin - meanError*2, 0, 0)
ppgplot.pgpt(dates, mag)
ppgplot.pgerrb(2, dates, mag, err, 0)
ppgplot.pgerrb(4, dates, mag, err, 0)
ppgplot.pglab("Days since %f"%startDate, "PTF mag", "%s [%d]"%(o.id, len(HJD)))
ppgplot.pgclos()
# Load the ephemerides
for o in objects:
hasEphemeris = o.loadEphemeris()
print o.ephemeris
# Write the object data to a textfile
for o in objects:
o.calculateOffsets()
o.calculateFluxes()
o.addWavelengths(wavelengthLookup)
PGPlotWindow = ppgplot.pgopen(arg.device)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(PGPlotWindow)
ppgplot.pgsci(1)
ppgplot.pgask(True)
for o in objects:
ppgplot.pgsch(1.6)
wavelengths, fluxes, fluxerrors, bands = o.getFluxData()
fluxMax = max(fluxes)
fluxMin = min(fluxes)
wavelengthMin = min(wavelengths)
wavelengthMax = max(wavelengths)
ppgplot.pgenv(500,25000 , 0, fluxMax*1.2, 0)
ppgplot.pglab("wavelength [\A]", "f\d\gn\u [mJy]", o.objectID)
ppgplot.pgsch(1.0)
ppgplot.pgpt(wavelengths, fluxes)
ppgplot.pgerrb(2, wavelengths, fluxes, fluxerrors, 0)
ppgplot.pgerrb(4, wavelengths, fluxes, fluxerrors, 0)
ppgplot.pgclos()
sys.exit()
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) ppgplot.pgpt(x_values, y_values, 1) ppgplot.pgslw(1) ppgplot.pgerrb(2, x_values, y_values, y_errors, 0) ppgplot.pgerrb(4, x_values, y_values, y_errors, 0) ppgplot.pglab(xLabel, yLabel, photometry["runName"]) ppgplot.pgclos() if not hasEphemeris: sys.exit() # Restrict the light-curve to a subset of phase phaseLimits = (0.51, 0.70) for photometry in allData:
ppgplot.pgenv(0, 2, numpy.max(mag), numpy.min(mag), 0, 0)
ppgplot.pglab("Phase", "PTF magnitude", "%s"%(arg.name))
ppgplot.pgsch(1.0)
ppgplot.pgpt(phases, mag)
ppgplot.pgerrb(2, phases, mag, err, 0)
ppgplot.pgerrb(4, phases, mag, err, 0)
ppgplot.pgsci(2)
model = modelledData.getColumn('mag')
model.extend(model)
ppgplot.pgsls(2)
ppgplot.pgslw(7)
ppgplot.pgline(phases, model)
"""
ppgplot.pgsch(1.6)
ppgplot.pgenv(0, 2, 0, maxFlux, 0, 0)
ppgplot.pglab("Phase", "PTF flux (mJy)", "%s"%(arg.name))
ppgplot.pgsch(1.0)
ppgplot.pgpt(phases, flux)
ppgplot.pgerrb(2, phases, flux, flux_err, 0)
ppgplot.pgerrb(4, phases, flux, flux_err, 0)
ppgplot.pgsci(2)
ppgplot.pgsls(2)
ppgplot.pgslw(7)
modelPhases = modelledData.getColumn('phase')
temp = copy.deepcopy(modelPhases)
for p in modelPhases: temp.append(p + 1.0)
modelPhases = temp
model = modelledData.getColumn('flux')
model = [m * 3631E3 for m in model]
def plothistsfr():
DATAMIN = -4.
DATAMAX = 15.
NBIN = int((DATAMAX-DATAMIN)*2.)
#print "ngal = ",len(g0.sfr)
ppgplot.pgbox("",0.0,0,"L",0.0,0)
ppgplot.pgenv(DATAMIN,DATAMAX,0,45,0)
ppgplot.pglab("SFR (h\d100\u\u-2\d M\d\(2281)\u yr\u-1 \d)","Number of Galaxies","")
ppgplot.pgsls(1)#dotted
ppgplot.pgslw(4) #line width
#ppgplot.pgsci(4)
#x=N.compress((abs(g0.ew) > ewmin),g0.sfr)
x=N.compress((g0.final > 0),g0.sfrc)
ppgplot.pghist(len(x),x,DATAMIN,DATAMAX,NBIN,5)
xlabel = 6.5
ylabel = 38.
ystep = 3.
dy=.4
dxl=3
dxr=.5
ppgplot.pgslw(deflw) #line width
ppgplot.pgtext(xlabel,ylabel,"CL1040")
xlin = N.array([xlabel-dxl,xlabel-dxr],'f')
ylin = N.array([ylabel+dy,ylabel+dy],'f')
ppgplot.pgslw(4) #line width
ppgplot.pgline(xlin,ylin)
ppgplot.pgslw(5)
ppgplot.pgsls(3)#dot-dash-dot-dash
#ppgplot.pgsci(3)
#x=N.compress((abs(g1.ew) > ewmin),g1.sfr)
x=N.compress((g1.final > 0),g1.sfrc)
ppgplot.pghist(len(x),x,DATAMIN,DATAMAX,NBIN,5)
ylabel = ylabel - ystep
xlin = N.array([xlabel-dxl,xlabel-dxr],'f')
ylin = N.array([ylabel+dy,ylabel+dy],'f')
ppgplot.pgline(xlin,ylin)
ppgplot.pgsls(1)
ppgplot.pgslw(deflw)
ppgplot.pgtext(xlabel,ylabel,"CL1054-12")
ppgplot.pgsls(1)#dot-dash-dot-dash
#ppgplot.pgsci(2)
ppgplot.pgslw(2) #line width
#x=N.compress((abs(g2.ew) > ewmin),g2.sfr)
x=N.compress((g2.final > 0),g2.sfrc)
ppgplot.pghist(len(x),x,DATAMIN,DATAMAX,NBIN,5)
ylabel = ylabel - ystep
ppgplot.pgslw(deflw) #line width
ppgplot.pgtext(xlabel,ylabel,"CL1216")
xlin = N.array([xlabel-dxl,xlabel-dxr],'f')
ylin = N.array([ylabel+dy,ylabel+dy],'f')
ppgplot.pgslw(2) #line width
ppgplot.pgline(xlin,ylin)
#print "Number in g2.ratios = ",len(g2.ratio)
#for ratio in g2.ratio:
# print ratio
#drawbinned(x,y,5)
ppgplot.pgsci(1)
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!"
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))
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
mainFluxMin = 1E99
for phase in numpy.arange(0.5, 1.6, 0.1):
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)
if (arg.preview):
bitmapView = {}
bitmapView['pgplotHandle'] = ppgplot.pgopen('/xs')
ppgplot.pgpap(8, 1)
ppgplot.pgenv(0.,fullFramexsize,0.,fullFrameysize, 1, 0)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
h = 6.626e-26 #erg s
k = 1.2807e-16 #erg K
l = N.arange(2000., 50000., 1000.) #wavelength in A
l = l * 1.e-8 #convert to cm
T = 40000. #K
B = 2. * h * c**2 / (l**5) / (N.exp(h * c / (l * k * T)) - 1) / 1.e14
l = l * 1.e4
xmin = 1.15 * min(l)
xmax = max(l)
ymin = min(B)
ymax = 1.2 * max(B)
my.psplotinit("blackbody.ps")
ppgplot.pgbox("", 0.0, 0, "", 0.0, 0)
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0, 0)
ppgplot.pglab("Wavelength", "Energy Output/second", "")
ppgplot.pgsci(4)
ppgplot.pgline(l, B)
ppgplot.pgtext(.6, 3., 'Star A')
ppgplot.pgtext(.6, -.5, 'Blue')
#T=20000.#K
#B=2.*h*c**2/(l**5)/(N.exp(h*c/(l*k*T))-1)
l = (l * 1.e-4 + 20000e-8) * 1.e4
ppgplot.pgsci(2)
ppgplot.pgline(l, B)
ppgplot.pgtext(2.6, 3., 'Star B')
ppgplot.pgtext(3.6, -.5, 'Red')
ppgplot.pgsci(1)
ppgplot.pgend()