def plot_candidate_sheet(rating_objects, pdm_cand_id, cand_ratings, description):
plot_utils.beginplot("cand_rating_report%s(%d).ps" % (currdatetime.strftime('%y%m%d'),pdm_cand_id), vertical=True)
ppgplot.pgtext(0,1,"%d: %s" % (pdm_cand_id, description))
top = 0.5
first = True
for ratobj, rating in zip(rating_objects,cand_ratings):
if (top - PARASPACING) < 0:
if first:
#
# Add P, DM, subints, subbands?
#
pfd = rating_utils.get_pfd_by_cand_id(pdm_cand_id)
prof = rating_utils.prep_profile(pfd)
ppgplot.pgsvp(0.10, 0.90, 0.60, 0.90)
ppgplot.pgswin(0, NUMPHASE, 1.1*np.min(prof), 1.1*np.max(prof))
ppgplot.pgbox('BCNTS', 0.25,5,'BC',0.0,0)
onephase = np.linspace(0,1,prof.size, endpoint=False)
manyphase = np.resize(onephase,prof.size*NUMPHASE)+np.arange(0,NUMPHASE).repeat(prof.size)
ppgplot.pgline(manyphase, np.resize(prof, (NUMPHASE*prof.size,)))
first = False
plot_utils.nextpage(vertical=True)
ppgplot.pgtext(0,1,"%d: %s (cont'd)" % (pdm_cand_id, description))
top = 0.9
ppgplot.pgtext(0,top, '%s: %s' % (ratobj.name, rating))
top -= PARASPACING
if first:
pfd = rating_utils.get_pfd_by_cand_id(pdm_cand_id)
prof = rating_utils.prep_profile(pfd)
ppgplot.pgsvp(0.10, 0.90, 0.60, 0.90)
ppgplot.pgswin(0, NUMPHASE, 1.1*np.min(prof), 1.1*np.max(prof))
ppgplot.pgbox('BCNTS', 0.25,5,'BC',0.0,0)
ppgplot.pgline(np.linspace(0,NUMPHASE,prof.size*NUMPHASE), np.resize(prof, (NUMPHASE*prof.size,)))
def _redrawHueGraph(self, hueI, widget, tupleToErase=None):
oldFill = widget.rootWidget.plotter.lowLevelPlotter.getFill()
oldColour = widget.rootWidget.plotter.lowLevelPlotter.getColour()
if not tupleToErase is None:
# Draw the start and end points in the frame bkg colour before drawing all points and lines in white:
colour = widget.parent.widgetPlotter.bgColour
widget.rootWidget.plotter.lowLevelPlotter.setColour(colour)
xs = nu.array([tupleToErase[0]])
ys = nu.array([tupleToErase[1]])
xs = widget.sizes[0].range.lo * (
1.0 - xs) + widget.sizes[0].range.hi * xs
ys = widget.sizes[1].range.lo * (
1.0 - ys) + widget.sizes[1].range.hi * ys
pgplot.pgpt(xs, ys, 0) # point type 0 is squares.
# Draw the canvas background:
widget.widgetPlotter.draw()
# Now draw the points and lines:
widget.rootWidget.plotter.lowLevelPlotter.setColour('white')
(xs, ys) = self.cm.hueGraphs[hueI].unpackValues()
xs = widget.sizes[0].range.lo * (1.0 -
xs) + widget.sizes[0].range.hi * xs
ys = widget.sizes[1].range.lo * (1.0 -
ys) + widget.sizes[1].range.hi * ys
pgplot.pgline(xs, ys)
pgplot.pgpt(xs, ys, 0) # point type 0 is squares.
widget.rootWidget.plotter.lowLevelPlotter.setFill(oldFill)
widget.rootWidget.plotter.lowLevelPlotter.setColour(oldColour)
def plot_selection_new(ident, dt=1.0, w=10.0):
dx, dy = ident.x1 - ident.x0, ident.y1 - ident.y0
ang = np.mod(np.arctan2(dy, dx), 2.0 * np.pi)
r = np.sqrt(dx**2 + dy**2)
drdt = r / (ident.t1 - ident.t0)
sa, ca = np.sin(ang), np.cos(ang)
dx = np.array([-dt, -dt, ident.t1 + dt, ident.t1 + dt, -dt]) * drdt
dy = np.array([w, -w, -w, w, w])
x = ca * dx - sa * dy + ident.x0
y = sa * dx + ca * dy + ident.y0
ppg.pgline(x, y)
ppg.pgpt1(x[0], y[0], 17)
ppg.pgpt1(x[1], y[1], 17)
ppg.pgsch(0.65)
ppg.pgslw(2)
if (x[0] < x[1]) & (ident.x0 < ident.x1):
ppg.pgptxt(x[1], y[1] - 1.5 * w, 0.0, 0.0, " %05d" % ident.norad)
else:
ppg.pgptxt(x[0], y[0] + 0.5 * w, 0.0, 0.0, " %05d" % ident.norad)
ppg.pgsch(1.0)
ppg.pgslw(1)
return
def plot(self, vmin, vmax, mpl=False, cmap=CMDEF, border=True):
"""
Elementary intensity plot using either matplotlib's imshow
or pgplot's pggray. Typically some setup may be needed
before and after this.
vmin -- image value for lowest intensity
vmax -- image value for highest intensity
mpl -- True for matplotlib, otherwise pgplot
cmap -- colour map if mpl
border -- plot a rectangular border around the outermost pixels or not
"""
if border:
x1, x2 = self.llx-0.5,self.llx+self.xbin*self.nx-0.5
y1, y2 = self.lly-0.5,self.lly+self.ybin*self.ny-0.5
if mpl:
limits = self.llx-0.5,self.llx+self.xbin*self.nx-0.5,self.lly-0.5,self.lly+self.ybin*self.ny-0.5
plt.imshow(self._data, cmap=cmap, interpolation='nearest', \
vmin=vmin, vmax=vmax, origin='lower', extent=limits)
if border:
plt.plot([x1,x2,x2,x1,x1],[y1,y1,y2,y2,y1])
else:
tr = np.array([self.llx-self.xbin,self.xbin,0,self.lly-self.ybin,0,self.ybin])
pg.pggray(self._data,0,self.nx-1,0,self.ny-1,vmax,vmin,tr)
if border:
pg.pgline([x1,x2,x2,x1,x1],[y1,y1,y2,y2,y1])
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 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 histogram(x, data, horizontal=True):
"""
plot a histogram of data. The bins' left edges
are given in x
"""
bin_coords = x.repeat(2)
data_coords = numpy.concatenate(([0], data.repeat(2), [0]))
if horizontal:
ppgplot.pgline(bin_coords, data_coords)
else:
ppgplot.pgline(data_coords, bin_coords)
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 plot_selection(id, x0, y0, dt=2.0, w=10.0):
dx, dy = id.x1 - id.x0, id.y1 - id.y0
ang = np.arctan2(dy, dx)
r = np.sqrt(dx**2 + dy**2)
drdt = r / (id.t1 - id.t0)
sa, ca = np.sin(ang), np.cos(ang)
dx = np.array([-dt, -dt, dt, dt, -dt]) * drdt
dy = np.array([w, -w, -w, w, w])
x = ca * dx - sa * dy + x0
y = sa * dx + ca * dy + y0
ppg.pgsci(7)
ppg.pgline(x, y)
return
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 linelabel(xlabel, ylabel, dx, ystep, dxl, dyl, label): #draw key
schdef = ppgplot.pgqch()
ppgplot.pgsch(1.1)
ppgplot.pgtext(xlabel, ylabel, label)
ppgplot.pgsch(1.1)
ylabel = ylabel - 2. * ystep
xs = N.arange(xlabel, (xlabel + dx), .01)
ys = ylabel * N.ones(len(xs), 'd')
ppgplot.pgsls(1)
ppgplot.pgline(xs, ys)
ppgplot.pgtext((xlabel + dxl + dx), (ylabel - dyl), "Memb")
ylabel = ylabel - ystep
ys = ylabel * N.ones(len(xs), 'd')
ppgplot.pgsls(4)
ppgplot.pgline(xs, ys)
ppgplot.pgtext((xlabel + dxl + dx), (ylabel - dyl), "Contam")
ppgplot.pgsch(schdef)
ppgplot.pgsls(1)
def plotsighaall(sig, psig, o2b, file, nbin):
o2b = N.array(o2b, 'f')
sig = N.array(sig, 'f')
psig = N.array(psig, 'f')
#o2b=o2b+4.
o2 = N.compress(o2b > -500., o2b)
sig = N.compress(o2b > -500., sig)
psig = N.compress(o2b > -500., psig)
psplot = file + ".ps"
psplotinit(psplot)
#ppgplot.pgsch(0.7)
ppgplot.pgslw(7)
(sigbin, o2bin) = my.binit(sig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgswin(-2., 2., -5., 20.)
ppgplot.pgbox('blcnst', 0.0, 0.0, 'bcvnst', 0.0,
0.0) #tickmarks and labeling
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "\gS\d10\u (gal/Mpc\u2\d)") #xlabel
ppgplot.pgsch(1.2)
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'EW(H\ga) (\(2078))')
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgpt(sigbin, o2bin, 17)
ppgplot.pgpt(N.log10(sig), o2, 1)
#my.errory(sigbin,o2bin,yerr)
#print 'dude2', sigbin, o2bin
ppgplot.pgsci(2)
ppgplot.pgline(sigbin, o2bin)
(sigbin, o2bin) = my.binit(psig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, o2bin, 21)
#my.errory(sigbin,o2bin,yerr)
ppgplot.pgsci(4)
ppgplot.pgline(sigbin, o2bin)
ppgplot.pgsci(1)
ppgplot.pgend()
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()
def plotsigo2all(sig, psig, o2b, file, nbin):
#o2=N.zeros(len(o2b),'f')
#for i in range(len(o2b)):
#print i, sig[i], psig[i], o2b[i]
# if o2b[i] < 0:
# o2[i]=-1*o2b[i]
#print "hey", o2[i]
o2 = o2b
psplot = file + ".ps"
psplotinit(psplot)
ppgplot.pgsch(0.7)
(sigbin, o2bin) = my.binit(sig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgswin(-1., 3., -.5, 10.)
ppgplot.pgbox('bcnst', 0.0, 0.0, 'bcvnst', 0.0,
0.0) #tickmarks and labeling
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "\gS\d10\u (gal/Mpc\u2\d)") #xlabel
ppgplot.pgsch(1.2)
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'EW([OII]) (\(2078))')
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgsci(2)
ppgplot.pgpt(sigbin, o2bin, 17)
#print 'dude2', sigbin, o2bin
ppgplot.pgline(sigbin, o2bin)
(sigbin, o2bin) = my.binit(psig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgsci(4)
ppgplot.pgpt(sigbin, o2bin, 21)
ppgplot.pgline(sigbin, o2bin)
ppgplot.pgsci(1)
ppgplot.pgend()
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)
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):
#/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()
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
y_values = continuum.flux
def plotxy(y, x=None, title=None, rangex=None, rangey=None, \
labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', symbol=ppgplot_symbol_, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, font=ppgplot_font_, logx=0, logy=0, \
logx2=0, logy2=0, errx=None, erry=None, id=0, noscale=0, \
aspect=0.7727, fontsize=ppgplot_font_size_, ticks='in', \
panels=[1,1], device=ppgplot_device_, setup=1):
"""
plotxy(y, ...)
An interface to make various XY style plots using PGPLOT.
'y' is the 1D sequence object to plot.
The optional entries are:
x: x values (default = 0, 1, ...)
title: graph title (default = None)
rangex: ranges for the x-axis (default = automatic)
rangey: ranges for the y-axis (default = automatic)
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))
errx: symmetric x errors (default = None)
erry: symmetric y errors (default = None)
symbol: symbol for points (default = None)
line: line style (default = 1 (solid))
width: line width (default = 1 (thin))
color: line and/or symbol color (default = 'white')
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))
noscale: turn off auto scaling (default = 0 (no))
aspect: aspect ratio (default = 0.7727 (rect))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
setup: Auto-setup the plot (default = 1)
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is an array
y = Num.asarray(y)
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, ppgplot_colors_
# Define the X axis limits if needed
if x is None: x = Num.arange(len(y), dtype='f')
else: x = Num.asarray(x)
# Determine the scaling to use for the first axis
if rangex is None: rangex = [x.min(), x.max()]
if rangey is None:
if noscale: rangey = [y.min(), y.max()]
else: rangey = scalerange(y)
# Prep the plotting device...
if (not ppgplot_dev_prep_ and setup):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, \
logx, logy, logx2, logy2, font, fontsize, \
id, aspect, ticks, panels, device=device)
# Choose the line color
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Plot symbols (and errors) if requested
if not symbol is None:
ppgplot.pgpt(x, y, symbol)
# Error bars
if errx is not None:
if not logx:
errx = Num.asarray(errx)
ppgplot.pgerrx(x + errx, x - errx, y, 1.0)
else:
errx = 10.0**Num.asarray(errx)
ppgplot.pgerrx(Num.log10(10.0**x + errx),
Num.log10(10.0**x - errx), y, 1.0)
if erry is not None:
if not logy:
erry = Num.asarray(erry)
ppgplot.pgerry(x, y + erry, y - erry, 1.0)
else:
erry = 10.0**Num.asarray(erry)
ppgplot.pgerry(x, Num.log10(10.0**y + erry),
Num.log10(10.0**y - erry), 1.0)
# Plot connecting lines if requested
if not line is None:
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
ppgplot.pgline(x, y)
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()
frequency, power = LombScargle(HJD, mag, err, fit_mean=True).autopower(minimum_frequency=0.01,maximum_frequency=20, samples_per_peak = 10, normalization='standard')
print len(frequency), "points in the periodogram"
#x = numpy.array(HJD)
#y = numpy.array(mag)
# Subtract the mean from the y-data
#y_mean = numpy.mean(y)
#y = y - y_mean
#periods = numpy.linspace(plo, phi, 1000)
#ang_freqs = 2 * numpy.pi / periods
#power = signal.lombscargle(x, y, ang_freqs)
# normalize the power
#N = len(x)
# power *= 2 / (N * y.std() ** 2)
ppgplot.pgenv(min(frequency), max(frequency), 0, max(power), 0, 0)
ppgplot.pgline(frequency, power)
#ppgplot.pgenv(min(periods), max(periods), 0, max(power), 0, 0)
#ppgplot.pgline(periods, power)
ppgplot.pglab("Frequency (d\u-1\d)", "Amplitude", "Lomb-Scargle: " + o.id)
bestFrequency = frequency[numpy.argmax(power)]
lc = ppgplot.pgqci()
ls = ppgplot.pgqls()
ppgplot.pgsci(3)
ppgplot.pgsls(2)
ppgplot.pgline([bestFrequency, bestFrequency], [0, max(power)])
ppgplot.pgsci(lc)
ppgplot.pgsls(ls)
print "Best frequency: %f cycles per day"%bestFrequency
bestPeriod = 1/bestFrequency
print "%s Best period: %f days or %f hours"%(o.id, bestPeriod, bestPeriod * 24.)
guess = numpy.array([a0, a1, a2]) x_values = continuumSpectrum.wavelengths y_values = continuumSpectrum.flux y_errors = numpy.ones(len(continuumSpectrum.flux)) results, covariance = scipy.optimize.curve_fit(quad, x_values, y_values, guess, ) errors = numpy.sqrt(numpy.diag(covariance)) # print "quadratic result:", results # print "quadratic errors:", errors a0 = results[0] a1 = results[1] a2 = results[2] xFit = spectrum.wavelengths yFit = quad(numpy.array(xFit), a0, a1, a2) ppgplot.pgsci(3) ppgplot.pgline(xFit, yFit) normalisedSpectrum = copy.deepcopy(spectrum) for index, w in enumerate(spectrum.wavelengths): # print w, spectrum.flux[index], yFit[index], spectrum.flux[index]/yFit[index] normalisedSpectrum.flux[index] = spectrum.flux[index]/yFit[index] lowerWavelength = min(normalisedSpectrum.wavelengths) upperWavelength = max(normalisedSpectrum.wavelengths) lowerFlux = min(normalisedSpectrum.flux) upperFlux = max(normalisedSpectrum.flux) ppgplot.pgslct(fitPGPlotWindow) ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0) ppgplot.pgsci(1) ppgplot.pgbin(normalisedSpectrum.wavelengths, normalisedSpectrum.flux)
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)
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, "Relative Power")
#fdot cut
ppgplot.pgsvp(margin + imfract, 1.0 - margin / 2, margin, margin + imfract)
ppgplot.pgswin(-0.1, 1.1, min(zs), max(zs))
ppgplot.pgbox("BCNST", 0.0, 0, "BCST", 0.0, 0)
ppgplot.pgline(fdotcut, zs)
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, "Relative Power")
# f-fdot image
ppgplot.pgsvp(margin, margin + imfract, margin, margin + imfract)
ppgplot.pgswin(min(rs), max(rs), min(zs), max(zs))
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, labx)
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, laby)
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
#! /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()
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()
guess = numpy.array([a0, a1, a2]) x_values = continuumSpectrum.wavelengths y_values = continuumSpectrum.flux y_errors = continuumSpectrum.fluxErrors results, covariance = scipy.optimize.curve_fit(quad, x_values, y_values, guess, ) errors = numpy.sqrt(numpy.diag(covariance)) # print "quadratic result:", results # print "quadratic errors:", errors a0 = results[0] a1 = results[1] a2 = results[2] xFit = spectrum.wavelengths yFit = quad(numpy.array(xFit), a0, a1, a2) ppgplot.pgsci(3) ppgplot.pgline(xFit, yFit) normalisedSpectrum = copy.deepcopy(spectrum) for index, w in enumerate(spectrum.wavelengths): # print w, spectrum.flux[index], yFit[index], spectrum.flux[index]/yFit[index] normalisedSpectrum.flux[index] = spectrum.flux[index]/yFit[index] normalisedSpectrum.fluxErrors[index] = spectrum.fluxErrors[index]/yFit[index] lowerWavelength = min(normalisedSpectrum.wavelengths) upperWavelength = max(normalisedSpectrum.wavelengths) lowerFlux = min(normalisedSpectrum.flux) upperFlux = max(normalisedSpectrum.flux) ppgplot.pgslct(fitPGPlotWindow) ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0) ppgplot.pgsci(1)
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
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()
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)
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, "Relative Power");
#fdot cut
ppgplot.pgsvp(margin+imfract, 1.0-margin/2, margin, margin+imfract)
ppgplot.pgswin(-0.1, 1.1, min(zs), max(zs))
ppgplot.pgbox("BCNST", 0.0, 0, "BCST", 0.0, 0)
ppgplot.pgline(fdotcut, zs)
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, "Relative Power");
# f-fdot image
ppgplot.pgsvp(margin, margin+imfract, margin, margin+imfract)
ppgplot.pgswin(min(rs), max(rs), min(zs), max(zs))
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, labx);
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, laby);
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
def plot_rating_sheet(rating):
"""
Plot a fact sheet on the ratings in the database corresponding to 'rating'.
'rating' is a dictionary of information from the MySQL database (as returned
by 'get_all_rating_types()'.
"""
plot_utils.beginplot("rating_report%s.ps" % currdatetime.strftime('%y%m%d'), vertical=True)
ch0 = ppgplot.pgqch()
ppgplot.pgsch(0.5)
ch = ppgplot.pgqch()
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 1 of 2" % (rating["name"], currdatetime.strftime('%c')))
ppgplot.pgsch(ch)
# Plot Histograms
all_ratings = get_ratings(rating["rating_id"])
range = xmin,xmax = np.min(all_ratings), np.max(all_ratings)
ppgplot.pgsci(1)
ppgplot.pgslw(1)
#===== Total/Classified/Unclassified
# Get data
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
(tot_counts, tot_left_edges)=np.histogram(all_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(tot_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, tot_counts)
(clsfd_counts, clsfd_left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3,4,5,6,7)), bins=NUMBINS, range=range)
ppgplot.pgsci(3) # plot classified in green
ppgplot.pgbin(tot_left_edges, clsfd_counts)
unclsfd_counts = tot_counts-clsfd_counts
ppgplot.pgsci(2) # plot unclassified in red
ppgplot.pgbin(tot_left_edges, unclsfd_counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Counts","")
#===== Class 1/2/3
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
(counts, left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3)), bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Class 1/2/3","")
#===== RFI
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
rfi_ratings = get_ratings(rating["rating_id"], human_classification=(4,))
(counts, left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI","")
#===== Noise
ppgplot.pgsvp(0.1, 0.9, 0.3, 0.45)
noise_ratings = get_ratings(rating["rating_id"], human_classification=(5,))
(counts, left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Noise","")
#===== Known/Harmonic
ppgplot.pgsvp(0.1, 0.9, 0.15, 0.3)
known_ratings = get_ratings(rating["rating_id"], human_classification=(6,7))
(counts, left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known/Harmonic","")
#===== Second page for differential histograms
plot_utils.nextpage(vertical=True)
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 2 of 2" % (rating["name"], currdatetime.strftime('%c')))
#===== RFI - Known
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
if rfi_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - known_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
#===== RFI - Noise
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
if noise_ratings.size==0 or rfi_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
#===== Known - Noise
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
if noise_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
# Y-axis label is taken care of outside of if/else (below)
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = known_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgswin(xmin,xmax,0,1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("NTS",0,5,"",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known - Noise","")
ppgplot.pgsch(ch0) # reset character height
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):
ppgplot.pgsci(1)
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)
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_", "")
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]
model.extend(model)
ppgplot.pgline(modelPhases, model)
ppgplot.pgsci(1)
ppgplot.pgsls(1)
ppgplot.pgslw(1)
# Plot the inset subplot showing the eclipse
subFlux = []
subFluxErr = []
subPhases = []
subModelPhases = []
subModel = []
startPhase = 0.96
endPhase = 1.04
print "maxFlux:", maxFlux
for index, phase in enumerate(phases):
if phase > startPhase and phase < endPhase:
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 = [] sharpness = [] random.seed() for n in range(arg.iterations): # Give all the points a random bump... y_perturbed = [] for y, y_error in zip(y_values, y_errors): y_p = numpy.random.normal(y, y_error)
def plotxy(y, x=None, title=None, rangex=None, rangey=None, \
labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', symbol=ppgplot_symbol_, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, font=ppgplot_font_, logx=0, logy=0, \
logx2=0, logy2=0, errx=None, erry=None, id=0, noscale=0, \
aspect=0.7727, fontsize=ppgplot_font_size_, ticks='in', \
panels=[1,1], device=ppgplot_device_, setup=1):
"""
plotxy(y, ...)
An interface to make various XY style plots using PGPLOT.
'y' is the 1D sequence object to plot.
The optional entries are:
x: x values (default = 0, 1, ...)
title: graph title (default = None)
rangex: ranges for the x-axis (default = automatic)
rangey: ranges for the y-axis (default = automatic)
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))
errx: symmetric x errors (default = None)
erry: symmetric y errors (default = None)
symbol: symbol for points (default = None)
line: line style (default = 1 (solid))
width: line width (default = 1 (thin))
color: line and/or symbol color (default = 'white')
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))
noscale: turn off auto scaling (default = 0 (no))
aspect: aspect ratio (default = 0.7727 (rect))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
setup: Auto-setup the plot (default = 1)
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is an array
y = Num.asarray(y);
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, ppgplot_colors_
# Define the X axis limits if needed
if x is None: x=Num.arange(len(y), dtype='f')
else: x = Num.asarray(x)
# Determine the scaling to use for the first axis
if rangex is None: rangex=[x.min(), x.max()]
if rangey is None:
if noscale: rangey=[y.min(), y.max()]
else: rangey=scalerange(y)
# Prep the plotting device...
if (not ppgplot_dev_prep_ and setup):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, \
logx, logy, logx2, logy2, font, fontsize, \
id, aspect, ticks, panels, device=device)
# Choose the line color
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Plot symbols (and errors) if requested
if not symbol is None:
ppgplot.pgpt(x, y, symbol)
# Error bars
if errx is not None:
if not logx:
errx = Num.asarray(errx)
if errx.size == 1:
errx = errx.repeat(x.size)
#ppgplot.pgerrx(x+errx, x-errx, y, 1.0)
ppgplot.pgerrb(5, x, y, errx, 1.0)
else:
errx = 10.0**Num.asarray(errx)
if errx.size == 1:
errx = errx.repeat(x.size)
#ppgplot.pgerrx(Num.log10(10.0**x + errx),
# Num.log10(10.0**x - errx), y, 1.0)
ppgplot.pgerrb(5, x, y, Num.log10(errx), 1.0)
if erry is not None:
if not logy:
erry = Num.asarray(erry)
if erry.size == 1:
erry = erry.repeat(y.size)
#ppgplot.pgerry(x, y+erry, y-erry, 1.0)
ppgplot.pgerrb(6, x, y, erry, 1.0)
else:
erry = 10.0**Num.asarray(erry)
if erry.size == 1:
erry = erry.repeat(y.size)
#ppgplot.pgerry(x, Num.log10(10.0**y + erry),
# Num.log10(10.0**y - erry), 1.0)
ppgplot.pgerrb(6, x, y, Num.log10(erry), 1.0)
# Plot connecting lines if requested
if not line is None:
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
ppgplot.pgline(x, y)
def main(args):
with open(os.path.join(os.path.dirname(__file__),
'precisiondata.cpickle')) as filedata:
exptimes, crosspoints, satpoints = pickle.load(filedata)
x_range = [9, 14]
interpcross = interp1d(exptimes, crosspoints, kind='linear')
interpsat = interp1d(exptimes, satpoints, kind='linear')
N = 5
colours = np.arange(2, 2 + N, 1)
exptimes = np.arange(1, N + 1) * 10
if args.besancon:
all_vmags = get_besancon_mag_data()
yhigh = 0.3
title = 'Besancon'
else:
all_vmags = get_nomad_mag_data()
yhigh = 0.4
title = 'NOMAD'
ytot = yhigh * len(all_vmags)
with pgh.open_plot(args.output):
pg.pgvstd()
pg.pgswin(x_range[0], x_range[1], 0, yhigh)
for exptime, colour in zip(exptimes, colours):
satpoint = interpsat(exptime)
crosspoint = interpcross(exptime)
selected = all_vmags[(all_vmags > satpoint) & (all_vmags <=
crosspoint)]
print(exptime, len(selected))
xdata, ydata = cumulative_hist(np.array(selected),
min_val=x_range[0], max_val=x_range[1], norm=len(all_vmags))
ydata /= float(len(all_vmags))
with pgh.change_colour(colour):
pg.pgbin(xdata, ydata, False)
pg.pgbox('bcnst', 0, 0, 'bcnst', 0, 0)
pg.pglab(r'V magnitude', 'High precision fraction', title)
# Label the right hand side
pg.pgswin(x_range[0], x_range[1], 0, ytot)
pg.pgbox('', 0, 0, 'smt', 0, 0)
pg.pgmtxt('r', 2., 0.5, 0.5, 'N')
#Â Create the legend
pg.pgsvp(0.7, 0.9, 0.1, 0.3)
pg.pgswin(0., 1., 0., 1.)
for i, (exptime, colour) in enumerate(zip(exptimes, colours)):
yval = 0.1 + 0.8 * i / len(exptimes)
with pgh.change_colour(colour):
pg.pgline(np.array([0.2, 0.4]), np.ones(2) * yval)
pg.pgtext(0.5, yval, r'{:d} s'.format(exptime))
upperFlambda = max(modelSpectrum.flux)
lowerFlambda = 0
lowerFlux = 0
upperFlux = 0
for s in modelSpectra:
fmax = max(s.flux)
if fmax>upperFlux:
upperFlux = fmax
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
colour = 1
for s in modelSpectra:
ppgplot.pgsci(colour)
ppgplot.pgline(s.wavelengths, s.flux)
plotx = 8500
ploty = s.getNearestFlux(plotx)
ppgplot.pgptxt(plotx, ploty, 0, 0, "%2.0f'"%(s.angle))
colour+= 1
if colour>15: colour = 1
ppgplot.pgsci(1)
label = "B=%.0f MG, T=%.1f keV"%(field, temperature)
ppgplot.pglab("wavelength", "i_0", label)
ppgplot.pgclos()
if arg.device!=None:
# Write to an additional output devide
mainPGPlotWindow = ppgplot.pgopen(arg.device)
else: device = "/xs"
pgramPlotWindow = ppgplot.pgopen(device)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(pgramPlotWindow)
ppgplot.pgask(True)
for o in objects:
HJD = o.getColumn('HJD')
mag = o.getColumn('mag')
err = o.getColumn('err')
frequency, power = LombScargle(HJD, mag, err).autopower(nyquist_factor=20)
bestFrequency = frequency[numpy.argmax(power)]
period = 1/bestFrequency
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)
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)
# 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)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
mainPGPlotWindow = ppgplot.pgopen('/xs')
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yUpper = 2.5
yLower = -0.5
newSpectra = []
for spectrum in spectra:
ppgplot.pgsci(1)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
ppgplot.pgenv(lowerWavelength, upperWavelength, yLower, yUpper, 0, 0)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
if hasEphemeris:
ppgplot.pglab("wavelength", "flux", "%s [%f]"%(spectrum.objectName, ephemeris.getPhase(spectrum.HJD)))
else:
ppgplot.pglab("wavelength", "flux", spectrum.objectName)
ppgplot.pgsls(3)
ppgplot.pgline( [tio1Range[0], tio1Range[0]], [yLower, yUpper])
ppgplot.pgline( [tio1Range[1], tio1Range[1]], [yLower, yUpper])
ppgplot.pgline( [tio2Range[0], tio2Range[0]], [yLower, yUpper])
ppgplot.pgline( [tio2Range[1], tio2Range[1]], [yLower, yUpper])
ppgplot.pgsls(1)
tio1measure = spectrum.integrate(wavelengthrange = tio1Range)
tio2measure = spectrum.integrate(wavelengthrange = tio2Range)
tioMeasuredRatio = tio1measure / tio2measure
def drawPolyLine(self, xs, ys):
if not self.plotDeviceIsOpened:
raise ValueError("You have not yet opened a PGPLOT device.")
pgplot.pgline(xs, ys)
(x, y) = s
ppgplot.pgcirc(x, y, 10)
# Now also draw the zoomed in region around the first aperture
if (applyShift):
(x, y) = masterApertureList[0]
croppedFrame = fullFrame[y-10:y+10, x-9:x+10]
ppgplot.pgslct(zoomedAperture)
rows, cols = numpy.shape(croppedFrame)
ppgplot.pggray(croppedFrame, 0, cols-1, 0, rows-1, 0, 255, pgPlotTransform)
ppgplot.pgsci(3)
ppgplot.pgsfs(2)
ppgplot.pgcirc(11, 11, 1)
xLine = [11, 11+xr]
yLine = [11, 11+yr]
ppgplot.pgline(xLine, yLine)
if arg.sleep!=0:
time.sleep(arg.sleep)
sys.stdout.write("\rProcessed %d frames \n"%frameRange)
sys.stdout.flush()
ppgplot.pgclos()
################################################################################################
""" We have run through all of the images now. """
################################################################################################
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()
for s1, s2 in zip(spectra1, spectra2):
combinedSpectrum = copy.deepcopy(s1)
combinedSpectrum.appendNewData(s2)
ppgplot.pgsci(1)
lowerWavelength = min(combinedSpectrum.wavelengths)
upperWavelength = max(combinedSpectrum.wavelengths)
lowerFlux = min(combinedSpectrum.flux)
upperFlux = max(combinedSpectrum.flux)
lowerFlux = 0
upperFlux = 2.5
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgsci(2)
ppgplot.pgline(s1.wavelengths, s1.flux)
ppgplot.pgsci(3)
ppgplot.pgline(s2.wavelengths, s2.flux)
ppgplot.pgsci(1)
if hasEphemeris:
ppgplot.pglab("wavelength", "flux", "%s [%f]"%(s1.objectName, ephemeris.getPhase(s1.HJD)))
else:
ppgplot.pglab("wavelength", "flux", s1.objectName)
if hasEphemeris:
filename = "combined_%f.json"%(ephemeris.getPhase(combinedSpectrum.HJD))
if arg.suffix!=None:
filename = "combined_%f_%s.json"%(ephemeris.getPhase(combinedSpectrum.HJD), arg.suffix)
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()
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!"
print "Number of connected bulbs", connectedCount
params = {
"color": "kelvin:2700",
# Graph of the x-direction
ppgplot.pgsvp(0.1, 0.7, 0.1, 0.2)
yMax = numpy.max(xCollapsed) * 1.1
yMin = numpy.min(xCollapsed) * 0.9
ppgplot.pgswin(-margins, margins, yMin, yMax)
ppgplot.pgsci(1)
ppgplot.pgbox('ABI', 1.0, 10, 'ABI', 0.0, 0)
xPoints = [x - margins for x in range(len(xCollapsed))]
ppgplot.pgsci(2)
ppgplot.pgbin(xPoints, xCollapsed, True)
numPolyPoints = 50
xFit = [float(i) * len(xCollapsed)/numPolyPoints for i in range(numPolyPoints)]
yFit = [xPoly[0]*x*x + xPoly[1]*x + xPoly[2] for x in xFit]
ppgplot.pgsci(3)
ppgplot.pgline([x - margins for x in xFit], yFit)
ppgplot.pgsls(2)
ppgplot.pgline([newxPeak-margins, newxPeak-margins], [yMin, yMax])
ppgplot.pgsci(4)
ppgplot.pgline([xBestOffset, xBestOffset], [yMin, yMax])
ppgplot.pgsls(1)
ppgplot.pgline(xGaussian, xGaussianFit)
# Graph of the y-direction
ppgplot.pgsvp(0.8, 0.9, 0.3, 0.9)
yMax = numpy.max(yCollapsed) * 1.1
yMin = numpy.min(yCollapsed) * 0.9
ppgplot.pgswin(yMin, yMax, -margins, margins)
ppgplot.pgsci(1)
ppgplot.pgbox('ABI', 0.0, 0, 'ABI', 1.0, 10)
xPoints = [x - margins for x in range(len(yCollapsed))]
print "initial guess", guess result, covariance = scipy.optimize.curve_fit(func, x_values, derivative_y, guess, derivative_y_errors) parameters = result errors = numpy.sqrt(numpy.diag(covariance)) print "Covariance:", covariance m_err = errors[0] c_err = errors[1] m_fit = parameters[0] c_fit = parameters[1] print "Fit m:%4.8f c:%4.8f"%(m_fit, c_fit), "errors:", m_err, c_err y0 = -c_fit/m_fit y0_error = y0 * math.sqrt((m_err/m_fit)**2 + (c_err/c_fit)**2) print "y0 - intercept:", y0, "[%f]"%y0_error, "BMJD: ", y0 + x_offset, "[%s]"%y0_error ppgplot.pgsci(3) ppgplot.pgline([min(x_values), max(x_values)], [func(min(x_values), m_fit, c_fit), func(max(x_values), m_fit, c_fit)]) 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: pnew = [] ynew = [] yenew = [] for (p, y, ye) in zip(photometry["phase"], photometry[yColumn], photometry[yErrors]): if p>phaseLimits[0] and p<phaseLimits[1]: pnew.append(p)
