def PlotElScan(fnamein, fnameout ):
eloff, tant, modtant = LoadElScanData(fnamein)
pyxplot.vect( [ eloff, eloff],
[ tant, modtant],
fnameout,
yax=pyxplot.axis(r"$T_{\rm Ant}\,({\rm K})$", xmin=82, xmax=90),
xax=pyxplot.axis(r"$\delta E\,({\rm arcmin})$",xmin=-1.5, xmax=1.5),
multi=True)
def CmpRandomV2():
obsd=pyfits.open("moonscand/obs79.fits")[1].data
m250 = pyfits.open("moonscand/model500rms.fits")[1].data
pyxplot.vect( [ obsd.field("dx") , m250.field("dx")] ,
[ obsd.field("fnu") - obsd.field("fnu").max() ,
m250.field("fnu") - m250.field("fnu").max()] ,
"plots/moonscanrandcom.eps",
xax=pyxplot.axis("Azimuth Offset (arcmin)",
xmin=None, xmax=None),
yax=pyxplot.axis("Rel Gain (B)"),
multi=1,
width=pyxplot.THESIS)
def PlotModDataR(modname, fnameout):
obsd=pyfits.open("moonscand/obs75-right.fits")[1].data
m250 = pyfits.open(modname)[1].data
pyxplot.vect( [ obsd.field("dx") , m250.field("dx")] ,
[ obsd.field("fnu") - obsd.field("fnu").max() ,
m250.field("fnu") - m250.field("fnu").max()] ,
fnameout,
xax=pyxplot.axis("Azimuth Offset (arcmin)",
xmin=None, xmax=None),
yax=pyxplot.axis("Rel Gain (B)"),
multi=1,
width=pyxplot.THESIS)
def PlotObsData():
dnorm= pyfits.open("moonscand/obs75_full.fits")[1].data
drand= pyfits.open("moonscand/obs79_full.fits")[1].data
pyxplot.vect( [ dnorm.field("dx") , drand.field("dx")] ,
[ dnorm.field("fnu") - dnorm.field("fnu").max() ,
drand.field("fnu") - drand.field("fnu").max()] ,
"plots/obsdata.eps",
xax=pyxplot.axis("Azimuth Offset (arcmin)",
xmin=None, xmax=None),
yax=pyxplot.axis("Rel Gain (B)",
xmin=-3),
multi=1,
width=pyxplot.THESIS)
def PlotSignal(fnamein, pixel_l, fnameout, hdu=1, trange=None):
din = pyfits.open(fnamein)[hdu].data
xv, yv = [], []
for c, r in pixel_l:
mask = SelectCR(din, c, r)
time = din.field("time")
if trange is not None:
mask = numarray.logical_and(mask, time >= trange[0])
mask = numarray.logical_and(mask, time < trange[1])
xv.append(time[mask])
yv.append(din.field("fnu")[mask])
pyxplot.vect(xv, yv, fnameout, multi=True, xax=pyxplot.axis(r"$t\,$(s)"), yax=pyxplot.axis(r"$F_{\nu}$"))
def PlotSFN(r,
fnameout,
fid,
**kwargs):
mask = numarray.logical_and( r[:,1] > 0,
r[:,2] > 0)
xvals= r[:,0]
yvals= (r[:,1])[mask]/(r[:,2])[mask]
m2= (xvals > 0)
yfid= 6.88* (xvals[m2]/float(fid))**(2.0/3)
pyxplot.vect( [xvals[mask], xvals[m2] ],
[yvals, yfid ],
fnameout,
xax=pyxplot.axis(r"$d$", type="log"),
yax=pyxplot.axis(r"$D(d)$", type="log", xmax=12),
multi=True)
def PlotF( fnamelist,
fnameout="plots/ruze.eps"):
xvv, yvv = [] , []
for fname in fnamelist:
data=GetData(fname)
xvv.append( data[0] )
yvv.append( data[1] )
print data[1].max() , data[1].min()
pyxplot.vect( xvv,
yvv,
fnameout,
xax=pyxplot.axis("Azimuth Offset (arcmin)",
xmin=-2, xmax=1),
yax=pyxplot.axis("Antenna T / Cal T"),
multi=1,
width=pyxplot.THESIS)
def PrintEfficiencies():
"Print predicted loss of efficiency due to total deformations"
res = []
for scandir , obsds in izip(allscans , obsscans) :
el=pyfits.open(obsds)[0].header["meanel"]
res.append( ( el,
OffsetEfficiency( scandir) ,
0.03,
scandir)
)
res.sort()
pyxplot.scatter( res,
"plots/offseteff.eps",
width=pyxplot.MNRAS_SC,
xax=pyxplot.axis(r"$E\,$(degrees)", xmin=0 , xmax=90),
yax=pyxplot.axis(r"$\eta_{\rm LSS}$"),
symbsize=0.1,
ploterrb=True)
def PrintEfficiencies():
"Print predicted loss of efficiency due to total deformations"
res = []
for scandir , obsds in izip(allscans , obsscans) :
el=pyfits.open(obsds)[0].header["meanel"]
res.append( ( el,
OffsetEfficiency( scandir) ,
scandir)
)
res.sort()
for x,y,z in res:
print x,y,z
pyxplot.scatter( res,
"plots/offseteff.eps",
width=pyxplot.THESIS,
xax=pyxplot.axis(r"$\theta\,$(deg)", xmin=0 , xmax=90),
yax=pyxplot.axis(r"$\epsilon_{\rm LSS}$"),
symbsize=0.05)
def plotSinlgeParDist(din,
parname,
fnameout,
burn=0):
"""
Plot the distribution of a single parameter
"""
d=din.data.field(parname)
x=d[burn*len(d):]
pyxplot.histogram( [ (x, "")],
fnameout,
xax=pyxplot.axis(escapeAxisName(parname)),
width=pyxplot.MNRAS_SC,
nbins=20,
key=None)
def ElHist():
"Compute histogram of measured elevations"
res = []
for scandir , obsds in izip(allscans , obsscans) :
el=pyfits.open(obsds)[0].header["meanel"]
res.append(el)
res.sort()
pyxplot.histogram( [ ( res , "" ) ],
"plots/el-hist.eps",
width=pyxplot.THESIS ,
key=None ,
nbins=9,
xax=pyxplot.axis(r"$\theta\,$(deg)", xmin=0 , xmax=90)
)
def PlotGain( d,
model,
utmin=0,
utmax=24,
mineff=0.2,
maxeff=0.5,
src=None):
def ProcD(d2):
pt=[]
md = {}
for x in d2:
if md.has_key(x[4] ):
md[x[4]].append ( ( x[1 ] , x[2][0] , x[2][1] ) )
else:
md[x[4]]= [ ( x[1 ] , x[2][0] , x[2][1] ) ]
for i,k in enumerate(md.keys()):
print "Series %i is %s " % ( i, k)
pt.append( pyxplot.graph.data.list( md[k], x=1, y=2, dy=3))
return pt
if src == None:
rcpnone = [ x for x in d if x[0]=="RCP" and x[3]==0 and x[5] > utmin and x[5] < utmax ]
rcpv1 = [ x for x in d if x[0]=="RCP" and x[3]==model and x[5] > utmin and x[5] < utmax ]
else:
rcpnone = [ x for x in d if x[0]=="RCP" and x[3]==0 and x[5] > utmin and x[5] < utmax and x[4] ==src ]
rcpv1 = [ x for x in d if x[0]=="RCP" and x[3]==model and x[5] > utmin and x[5] < utmax and x[4]==src]
pt = [ pyxplot.graph.data.list( rcpnone, x=1, y=2, dy=3),
pyxplot.graph.data.list( rcpv1, x=1, y=2, dy=3),
]
c = canvas.canvas()
gon = c.insert(pyxplot.graph.graphxy(width=pyxplot.MNRAS_SC,
x=pyxplot.axis(r"$E\,$(degrees)",
xmin=0, xmax=90) ,
y=pyxplot.axis(r"$\eta_{\rm a}$",
xmin=mineff, xmax=maxeff),
))
gon.plot(ProcD(rcpv1),
[ graph.style.symbol(graph.style.symbol.changesquare,
symbolattrs=[deco.filled()]
),
graph.style.errorbar()
]
)
goff = c.insert(pyxplot.graph.graphxy(width=pyxplot.MNRAS_SC,
ypos =gon.height+0.5,
x=graph.axis.linkedaxis(gon.axes["x"]),
y=pyxplot.axis(r"$\eta_{\rm a}$",
xmin=mineff, xmax=maxeff),
))
goff.plot(ProcD(rcpnone),
[ graph.style.symbol(graph.style.symbol.changesquare),
graph.style.errorbar()
]
)
c.writetofile("plots/gaincurve.eps")
c.writetofile("plots/gaincurve.pdf")
gon=pyxplot.graph.graphxy(width=pyxplot.MNRAS_SC,
x=pyxplot.axis(r"$E\,$(degrees)",
xmin=0, xmax=90) ,
y=pyxplot.axis(r"$\eta_{\rm a}$",
xmin=mineff, xmax=maxeff),
)
gon.plot(ProcD(rcpv1),
[ graph.style.symbol(graph.style.symbol.changesquare,
symbolattrs=[deco.filled()]
),
graph.style.errorbar()
]
)
gon.writetofile("plots/gaincurve_on.pdf")
goff = pyxplot.graph.graphxy(width=pyxplot.MNRAS_SC,
x=pyxplot.axis(r"$E\,$(degrees)",
xmin=0, xmax=90) ,
y=pyxplot.axis(r"$\eta_{\rm a}$",
xmin=mineff, xmax=maxeff),
)
goff.plot(ProcD(rcpnone),
[ graph.style.symbol(graph.style.symbol.changesquare),
graph.style.errorbar()
]
)
goff.writetofile("plots/gaincurve_off.pdf")
