def interpolate(points, t):
"""points=(x,y), list of x's and y's. "t" ... list of x, for which we want
to evaluate the dependence "y". returns a list of "y" evaluated at the grid
"t".
Assumes t is sorted in ascending order
Assumes points x axis are sorted in ascending order
"""
xx, yy = points
x = t
t0 = numarray.compress(x <= min(xx), x)
# t1 = numarray.compress( (x>min(xx)) & (x<max(xx)), x )
t1 = numarray.compress((x > min(xx)) & (x < xx[-1]), x)
# t2 = numarray.compress(x>=max(xx), x)
t2 = numarray.compress(x >= xx[-1], x)
if (xx[1] - xx[0] == 0):
slope0 = sign(yy[1] - yy[0]) * 1e20
else:
slope0 = (yy[1] - yy[0]) / (xx[1] - xx[0])
slope2 = (yy[-1] - yy[-2]) / (xx[-1] - xx[-2])
indices = numarray.searchsorted(xx, t1)
x0 = xx[indices - 1]
x1 = xx[indices]
y0 = yy[indices - 1]
y1 = yy[indices]
slope = (y1 - y0) / (x1 - x0)
y1 = slope * (t1 - x0) + y0
y0 = slope0 * (t0 - xx[0]) + yy[0] # extrapolate
y2 = slope2 * (t2 - xx[-1]) + yy[-1] # extrapolate
y = numarray.concatenate((y0, y1, y2))
return y
def interpolate(points,t):
"""points=(x,y), list of x's and y's. "t" ... list of x, for which we want
to evaluate the dependence "y". returns a list of "y" evaluated at the grid
"t".
Assumes t is sorted in ascending order
Assumes points x axis are sorted in ascending order
"""
xx,yy=points
x=t
t0 = numarray.compress(x<=min(xx), x)
# t1 = numarray.compress( (x>min(xx)) & (x<max(xx)), x )
t1 = numarray.compress( (x>min(xx)) & (x<xx[-1]), x )
# t2 = numarray.compress(x>=max(xx), x)
t2 = numarray.compress(x>=xx[-1], x)
if (xx[1]-xx[0] == 0):
slope0 = sign(yy[1]-yy[0])*1e20
else:
slope0 = (yy[1]-yy[0])/(xx[1]-xx[0])
slope2 = (yy[-1]-yy[-2])/(xx[-1]-xx[-2])
indices = numarray.searchsorted(xx,t1)
x0 = xx[indices-1]
x1 = xx[indices]
y0 = yy[indices-1]
y1 = yy[indices]
slope = (y1-y0)/(x1-x0)
y1 = slope*(t1-x0)+y0
y0 = slope0*(t0-xx[0])+yy[0] # extrapolate
y2 = slope2*(t2-xx[-1])+yy[-1] # extrapolate
y = numarray.concatenate((y0,y1,y2))
return y
def binitbins(xmin, xmax, nbin, x, y): #use equally spaced bins
dx = float((xmax - xmin) / (nbin))
xbin = N.arange(xmin, (xmax), dx) + dx / 2.
#print "within binitbins"
#print "xbin = ",xbin
#print "dx = ",dx
#print "xmax = ",xmax
#print "xmin = ",xmin
ybin = N.zeros(len(xbin), 'd')
ybinerr = N.zeros(len(xbin), 'd')
xbinnumb = N.array(len(x), 'd')
x1 = N.compress((x >= xmin) & (x <= xmax), x)
y1 = N.compress((x >= xmin) & (x <= xmax), y)
x = x1
y = y1
xbinnumb = ((x - xmin) * nbin / (xmax - xmin)
) #calculate x bin number for each point
j = -1
for i in range(len(xbin)):
ydata = N.compress(abs(xbinnumb - float(i)) < .5, y)
try:
ybin[i] = N.average(ydata)
#ybin[i]=pylab.median(ydata)
ybinerr[i] = pylab.std(ydata) / N.sqrt(float(len(ydata)))
except ZeroDivisionError:
ybin[i] = 0.
ybinerr[i] = 0.
return xbin, ybin, ybinerr
def plot_source_info(self,event):
ra = event.xdata
dec = event.ydata
#print (event.key,ra,dec)
if event.key == 's':
#print self.constructed_pos[:,0]
#print self.constructed_pos[:,1]
dist = numarray.sqrt( (self.constructed_pos[:,0] - ra)**2 + (self.constructed_pos[:,1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.constructed_source_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.constructed_source_list[the_source_ind[0]]
print the_source
dist = numarray.sqrt( (the_source.current_pos[0] - self.real_pos[:,0])**2 + (the_source.current_pos[1] - self.real_pos[:,1])**2)
print "min distances to nearest real source = %f arcsec" % min(dist)
the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list))))
the_source = self.real_list[the_source_ind[0]]
print "That real source is at ra=%f dec=%f" % (the_source.start_pos[0],the_source.start_pos[1])
if event.key == 'r':
dist = numarray.sqrt( (self.real_pos[:,0] - ra)**2 + (self.real_pos[:,1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(dist == min(dist), numarray.fromlist(range(len(self.real_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.real_list[the_source_ind[0]]
print the_source
def drawmeridians(self,ax,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1]):
"""
draw meridians (longitude lines).
ax - current axis instance.
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
"""
if self.projection not in ['merc','cyl']:
lats = N.arange(-80,81).astype('f')
else:
lats = N.arange(-90,91).astype('f')
xdelta = 0.1*(self.xmax-self.xmin)
ydelta = 0.1*(self.ymax-self.ymin)
for merid in meridians:
lons = merid*N.ones(len(lats),'f')
x,y = self(lons,lats)
# remove points outside domain.
testx = N.logical_and(x>=self.xmin-xdelta,x<=self.xmax+xdelta)
x = N.compress(testx, x)
y = N.compress(testx, y)
testy = N.logical_and(y>=self.ymin-ydelta,y<=self.ymax+ydelta)
x = N.compress(testy, x)
y = N.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:]-x[0:-1])**2
yd = (y[1:]-y[0:-1])**2
dist = N.sqrt(xd+yd)
split = dist > 500000.
if N.sum(split) and self.projection not in ['merc','cyl']:
ind = (N.compress(split,MLab.squeeze(split*N.indices(xd.shape)))+1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x,y in zip(xl,yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,y,linewidth=linewidth,linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
def lsq_clipped(x, y, sig, int_scat=None, clip=3.0, astart=None, bstart=None,
siga_in=0.0, sigb_in=0.0):
olda = oldb = None
if sig is None:
sig = N.ones(x.shape)
mwt = 0
else:
sig = N.array(sig)
mwt = 1
a = astart
b = bstart
while 1:
if astart is None and bstart is None:
if int_scat is None:
results = nr.fit(x, y, sig)
else:
results = nr.fit_i(x, y, sig, int_scat)
a, b, siga, sigb, chi2 = results
scatter = calc_scatter(x, y, sig, int_scat, a, b)
elif astart is not None and bstart is None:
if int_scat is None:
results = nr.fit_slope(x, y, sig, mwt, astart, siga_in)
else:
results = nr.fit_slope_i(x, y, sig, int_scat, astart, siga_in)
b, sigb, chi2 = results
siga = 0.0
scatter = calc_scatter(x, y, sig, int_scat, astart, b)
elif astart is None and bstart is not None:
if int_scat is None:
results = nr.fit_intercept(x, y, sig, mwt, bstart, sigb_in)
else:
results = nr.fit_intercept_i(x, y, sig, int_scat, bstart, sigb_in)
a, siga, chi2 = results
sigb = 0.0
scatter = calc_scatter(x, y, sig, int_scat, a, bstart)
if not (olda is None or oldb is None):
if abs(a) < conv_limit:
atest = conv_limit
else:
atest = abs(a)
if abs(b) < conv_limit:
btest = conv_limit
else:
btest = abs(b)
if (abs(olda - a) < conv_limit*atest and
abs(oldb - b) < conv_limit*btest):
break
keep = abs(y - a - b*x) < clip*scatter
xnew = N.compress(keep, x)
ynew = N.compress(keep, y)
signew = N.compress(keep, sig)
x = xnew
y = ynew
sig = signew
olda, oldb = (a, b)
n = len(x)
return (a, b, siga, sigb, chi2, scatter, n)
def calcavesky():
input = open("noise.dat", 'r')
aperture = []
counts = []
area = []
j = 0
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('mosaic_minus') > -1: #skip lines with '#' in them
j = 0
continue
j = j + 1
if (j > 3):
#print j, line
t = line.split()
aperture.append(float(t[0]))
counts.append(float(t[1]))
area.append(float(t[2]))
input.close()
aperture = N.array(aperture, 'f')
counts = N.array(counts, 'f')
area = N.array(area, 'f')
ap = N.zeros(npoints, 'f')
aparea = N.zeros(nap, 'f')
aveap = N.zeros(nap, 'f')
aveaperr = N.zeros(nap, 'f')
avearea = N.zeros(nap, 'f')
aveareaerr = N.zeros(nap, 'f')
#for i in range(len(ap)):
for i in range(nap):
#print i, len(ap),aperture[i],aperture[i+1]
if (i < (nap - 1)):
ap = N.compress(
(aperture >= aperture[i]) & (aperture < aperture[i + 1]),
counts)
aparea = N.compress(
(aperture >= aperture[i]) & (aperture < aperture[i + 1]), area)
else:
ap = N.compress((aperture >= aperture[i]) & (aperture < 20.),
counts)
aparea = N.compress((aperture >= aperture[i]) & (aperture < 20.),
area)
#print ap
#aparea=N.compress((aperture >= aperture[i]) & (aperture < aperture[i+1]),area)
aveap[i] = N.average(ap)
aveaperr[i] = scipy.stats.std(ap)
avearea[i] = N.average(aparea)
aveareaerr[i] = scipy.stats.std(aparea)
print "ave sky = %8.4f +/- %8.4f" % (N.average(ap),
scipy.stats.std(ap))
print "ave area = %8.4f +/- %8.4f" % (N.average(aparea),
scipy.stats.std(aparea))
return aveap, aveaperr, avearea, aveareaerr
def fig5b(t,m,p): # Metallicity vs. time
""" Plot SFR vs age """
tt = numarray.compress(t>p.s0x,t)
mm = numarray.compress(t>p.s0x,m)
tt = numarray.compress(tt<p.s2x,tt)
mm = numarray.compress(tt<p.s2x,mm)
pylab.plot(tt,mm)
metallicity_bezier(p)
pylab.xlabel("Age (Gyr)")
pylab.ylabel("Metallicity")
def fig5(t, m): # Metallicity vs. time
""" Plot SFR vs age """
tt = numarray.compress(t > p.s0x, t)
mm = numarray.compress(t > p.s0x, m)
tt = numarray.compress(tt < p.s2x, tt)
mm = numarray.compress(tt < p.s2x, mm)
pylab.plot(10.**tt / 1.e9, mm)
pylab.xlim(0.1, 15)
pylab.xlabel("Age (Gyr)")
pylab.ylabel("Metallicity")
def fig5b(t, m, p): # Metallicity vs. time
""" Plot SFR vs age """
tt = numarray.compress(t > p.s0x, t)
mm = numarray.compress(t > p.s0x, m)
tt = numarray.compress(tt < p.s2x, tt)
mm = numarray.compress(tt < p.s2x, mm)
pylab.plot(tt, mm)
metallicity_bezier(p)
pylab.xlabel("Age (Gyr)")
pylab.ylabel("Metallicity")
def fig5(t,m): # Metallicity vs. time
""" Plot SFR vs age """
tt = numarray.compress(t>p.s0x,t)
mm = numarray.compress(t>p.s0x,m)
tt = numarray.compress(tt<p.s2x,tt)
mm = numarray.compress(tt<p.s2x,mm)
pylab.plot(10.**tt/1.e9,mm)
pylab.xlim(0.1,15)
pylab.xlabel("Age (Gyr)")
pylab.ylabel("Metallicity")
def med_func(x, y, sig, b):
small = 1.0e-8
aa = median(y - b*x)
d = (y - aa - b*x)
mad = median(N.absolute(d))
s = mad / 0.6745
d /= sig
sign = N.compress(N.absolute(d) > small, d)
sign = sign / N.absolute(sign)
x = N.compress(N.absolute(d) > small, x)
sum = N.sum(sign * x)
return sum, s, aa
def contour(x, y, xmin, xmax, ymin, ymax):
ncontbin = 20.
dx = float(abs((xmax - xmin) / ncontbin))
dy = float(abs((ymax - ymin) / ncontbin))
xcontour = N.arange(xmin, (xmax), dx)
ycontour = N.arange(ymin, (ymax), dy)
A = N.zeros((int(ncontbin), int(ncontbin)), 'f')
xbinnumb = N.array(len(x), 'f')
ybinnumb = N.array(len(x), 'f')
x1 = N.compress((x >= xmin) & (x <= xmax) & (y >= ymin) & (y <= ymax), x)
y1 = N.compress((x >= xmin) & (x <= xmax) & (y >= ymin) & (y <= ymax), y)
x = x1
y = y1
xbinnumb = ((x - xmin) * ncontbin / (xmax - xmin)
) #calculate x bin number for each point
ybinnumb = ((y - ymin) * ncontbin / (ymax - ymin)
) #calculate x bin number for each point
binID = zip(xbinnumb, ybinnumb)
Amax = 0
for (i1, j1) in binID:
i = int(i1)
j = int(j1)
if i > (ncontbin - 1):
continue
if j > (ncontbin - 1):
continue
#print i,j,A
A[j,
i] = A[j,
i] + 1 #have to switch i,j in A[] to make contours look right
#pylab.figure()
#print A
for i in range(int(ncontbin)):
for j in range(int(ncontbin)):
if A[j, i] > Amax:
Amax = A[j, i]
#Amax=max(max(A))
print "max of A = ", Amax
V = N.array([.025, .1, .25, .5, .75, .95], 'f')
V = V * Amax
con = pylab.contour((xcontour + dx), (ycontour),
A,
V,
alpha=5.,
colors='r',
linewidth=5.,
hold='on')
def azhistsub(self,x,y,m1,color): m2=m1-2. y1=N.compress((x< m1) & (x > m2),y) (n,bin,patches)=pylab.hist(y1,bins=10,normed=1,fill=False,ec=color,fc='w',alpha=1.,visible=False) s=color+'-' s1=str(m1)+'> M > '+str(m2) pylab.plot(bin,n,s,label=s1)
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 plot_source_info(self, event):
ra = event.xdata
dec = event.ydata
#print (event.key,ra,dec)
if event.key == 's':
#print self.constructed_pos[:,0]
#print self.constructed_pos[:,1]
dist = numarray.sqrt((self.constructed_pos[:, 0] - ra)**2 +
(self.constructed_pos[:, 1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(
dist == min(dist),
numarray.fromlist(range(len(self.constructed_source_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.constructed_source_list[the_source_ind[0]]
print the_source
dist = numarray.sqrt(
(the_source.current_pos[0] - self.real_pos[:, 0])**2 +
(the_source.current_pos[1] - self.real_pos[:, 1])**2)
print "min distances to nearest real source = %f arcsec" % min(
dist)
the_source_ind = numarray.compress(
dist == min(dist),
numarray.fromlist(range(len(self.real_list))))
the_source = self.real_list[the_source_ind[0]]
print "That real source is at ra=%f dec=%f" % (
the_source.start_pos[0], the_source.start_pos[1])
if event.key == 'r':
dist = numarray.sqrt((self.real_pos[:, 0] - ra)**2 +
(self.real_pos[:, 1] - dec)**2)
#print dist
#print "min distance = %f " % min(dist)
the_source_ind = numarray.compress(
dist == min(dist),
numarray.fromlist(range(len(self.real_list))))
#print the_source_ind
#the_source_ind = numarray.compress(dist == min(dist),numarray.arange(len(self.constructed_source_list)))
the_source = self.real_list[the_source_ind[0]]
print the_source
def itmean(arr,darr,thresh):
arr=numarray.array(arr)
darr=numarray.array(darr)
print len(arr),len(darr)
arr=numarray.compress(darr>0.,arr)
darr=numarray.compress(darr>0.,darr)
arr2=arr
darr2=darr
m=numarray.sum(arr/darr)/numarray.sum(1./darr)
lold=0
l=len(arr)
while(l!=lold):
arr2=numarray.compress(abs(arr-m)<thresh,arr)
darr2=numarray.compress(abs(arr-m)<thresh,darr)
m=numarray.sum(arr2/darr2)/numarray.sum(1./darr2)
lold=l
l=len(arr2)
print numarray.sum(arr/darr)/numarray.sum(1./darr), numarray.sum(arr2/darr2)/numarray.sum(1./darr2),len(arr2),len(arr)
return numarray.sum(arr2/darr2)/numarray.sum(1./darr2)
def itmean(arr, darr, thresh):
arr = numarray.array(arr)
darr = numarray.array(darr)
print len(arr), len(darr)
arr = numarray.compress(darr > 0., arr)
darr = numarray.compress(darr > 0., darr)
arr2 = arr
darr2 = darr
m = numarray.sum(arr / darr) / numarray.sum(1. / darr)
lold = 0
l = len(arr)
while (l != lold):
arr2 = numarray.compress(abs(arr - m) < thresh, arr)
darr2 = numarray.compress(abs(arr - m) < thresh, darr)
m = numarray.sum(arr2 / darr2) / numarray.sum(1. / darr2)
lold = l
l = len(arr2)
print numarray.sum(arr / darr) / numarray.sum(1. / darr), numarray.sum(
arr2 / darr2) / numarray.sum(1. / darr2), len(arr2), len(arr)
return numarray.sum(arr2 / darr2) / numarray.sum(1. / darr2)
def azmr(self):
x=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -18.),self.Mabs)
y=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -18.),self.ar)
x1=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -20.38),self.Mabs)
y1=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -20.38),self.ar)
y=2.5*N.log10(y)
#pylab.plot(x,y,'k.',markersize=0.1,zorder=1)
print "average Ar for Mr < -20.38 = %5.2f +/- %5.2f"%(N.average(y1),pylab.std(y1))
(xbin,ybin)=my.binit(x1,y1,20)
#(xbin,ybin,ybinerr)=my.biniterr(x,y,20)
for i in range(len(xbin)):
print i,xbin[i],ybin[i]
print "Average of binned values = ",N.average(ybin)
print "average Ar for Mr < -20.38 = %5.2f +/- %5.2f"%(N.average(N.log10(y1)),pylab.std(N.log10(y1)))
#pylab.axis([-26.,-12.,0.1,30.])
pylab.xlabel(r'$\rm{M_r}$',fontsize=28.)
pylab.ylabel(r'$\rm{A_r}$',fontsize=28.)
(xbin,ybin)=my.binit(x,y,20)
#(xbin,ybin,ybinerr)=my.biniterr(x,y,20)
for i in range(len(xbin)):
print i,xbin[i],ybin[i]
pylab.plot(xbin,ybin,'r-',lw=5)
ax=pylab.gca()
xmin=-24.
xmax=-18.
ymin=-1.
ymax=3.
my.contourf(x,y,xmin,xmax,ymin,ymax)
pylab.axvline(x=-20.6,linewidth=3,ls='--',c='g')
xl=N.arange(-23.,-20.5,.2)
yl=0.76*N.ones(len(xl),'f')
pylab.plot(xl,yl,'b-',lw=3)
pylab.axis([-24.,-18,-1.,2.4])
#ax.set_yscale('log')
#pylab.show()
pylab.savefig('armr.eps')
print "fraction w/MPA stellar mass and Az = ",N.sum(self.mpaflag)/(1.*len(self.mpaflag))
def azhist(self):
x=N.compress((self.mpaflag > 0.1) & (self.ew > 4.),self.Mabs)
y=N.compress((self.mpaflag > 0.1) & (self.ew > 4.),self.ar)
y=2.5*N.log10(y)
m1=-18.
self.azhistsub(x,y,m1,'b')
#m1=-19.
#self.azhistsub(x,y,m1,'r')
m1=-20.
self.azhistsub(x,y,m1,'g')
#m1=-21.
#self.azhistsub(x,y,m1,'k')
m1=-22.
self.azhistsub(x,y,m1,'y')
#m1=-23.
#self.azhistsub(x,y,m1,'c')
#pylab.show()
#ax=pylab.gca()
#ax.set_xscale('log')
pylab.legend(loc='upper right')
pylab.savefig('azhist.eps')
def itmed(arr,thresh):
arr=numarray.array(arr)
arr2=arr
m=numarray.mlab.median(arr)
lold=0
l=len(arr)
while(l!=lold):
arr2=numarray.compress(abs(arr-m)<thresh,arr)
m=numarray.mlab.median(arr2)
lold=l
l=len(arr2)
print numarray.mlab.median(arr), numarray.mlab.median(arr2),len(arr2),len(arr)
return numarray.mlab.median(arr2)
def itmed(arr, thresh):
arr = numarray.array(arr)
arr2 = arr
m = numarray.mlab.median(arr)
lold = 0
l = len(arr)
while (l != lold):
arr2 = numarray.compress(abs(arr - m) < thresh, arr)
m = numarray.mlab.median(arr2)
lold = l
l = len(arr2)
print numarray.mlab.median(arr), numarray.mlab.median(arr2), len(
arr2), len(arr)
return numarray.mlab.median(arr2)
def biwt_func(x, y, sig, b): # Problems?!?
aa = median(y - b*x)
d = (y - aa - b*x)
mad = median(N.absolute(d))
s = mad / 0.6745
d /= sig
# biweight
c = 6.0
f = d*(1-d**2/c**2)**2
sum = N.sum(N.compress(N.absolute(d) <= c, x*f))
# lorentzian
#f = d/(1+0.5*d**2)
#sum = N.sum(x*f)
# MAD
#small = 1.0e-8
#sign = N.compress(N.absolute(d) > small, d)
#sign = sign / N.absolute(sign)
#sum = N.sum(N.compress(N.absolute(d) > small, x)*sign)
return sum, s, aa
def find_closest(input_array, target_array, tol):
"""
Find the set of elements in input_array that are closest to
elements in target_array. Record the indices of the elements in
target_array that are within tolerance, tol, of their closest
match. Also record the indices of the elements in target_array
that are outside tolerance, tol, of their match.
For example, given an array of observations with irregular
observation times along with an array of times of interest, this
routine can be used to find those observations that are closest to
the times of interest that are within a given time tolerance.
NOTE: input_array must be sorted! The array, target_array, does not have to be sorted.
Inputs:
input_array: a sorted Float64 numarray
target_array: a Float64 numarray
tol: a tolerance
Returns:
closest_indices: the array of indices of elements in input_array that are closest to elements in target_array
accept_indices: the indices of elements in target_array that have a match in input_array within tolerance
reject_indices: the indices of elements in target_array that do not have a match in input_array within tolerance
"""
input_array_len = len(input_array)
closest_indices = numarray.searchsorted(input_array, target_array) # determine the locations of target_array in input_array
acc_rej_indices = [-1] * len(target_array)
curr_tol = [tol] * len(target_array)
est_tol = 0.0
for i in xrange(len(target_array)):
best_off = 0 # used to adjust closest_indices[i] for best approximating element in input_array
if closest_indices[i] >= input_array_len:
# the value target_array[i] is >= all elements in input_array so check whether it is within tolerance of the last element
closest_indices[i] = input_array_len - 1
est_tol = target_array[i] - input_array[closest_indices[i]]
if est_tol < curr_tol[i]:
curr_tol[i] = est_tol
acc_rej_indices[i] = i
elif target_array[i] == input_array[closest_indices[i]]:
# target_array[i] is in input_array
est_tol = 0.0
curr_tol[i] = 0.0
acc_rej_indices[i] = i
elif closest_indices[i] == 0:
# target_array[i] is <= all elements in input_array
est_tol = input_array[0] - target_array[i]
if est_tol < curr_tol[i]:
curr_tol[i] = est_tol
acc_rej_indices[i] = i
else:
# target_array[i] is between input_array[closest_indices[i]-1] and input_array[closest_indices[i]]
# and closest_indices[i] must be > 0
top_tol = input_array[closest_indices[i]] - target_array[i]
bot_tol = target_array[i] - input_array[closest_indices[i]-1]
if bot_tol <= top_tol:
est_tol = bot_tol
best_off = -1 # this is the only place where best_off != 0
else:
est_tol = top_tol
if est_tol < curr_tol[i]:
curr_tol[i] = est_tol
acc_rej_indices[i] = i
if est_tol <= tol:
closest_indices[i] += best_off
accept_indices = numarray.compress(numarray.greater(acc_rej_indices, -1), acc_rej_indices)
reject_indices = numarray.compress(numarray.equal(acc_rej_indices, -1), numarray.arange(len(acc_rej_indices)))
return (closest_indices, accept_indices, reject_indices)
def drawmeridians(self,ax,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1],labels=[0,0,0,0],\
font='rm',fontsize=12):
"""
draw meridians (longitude lines).
ax - current axis instance.
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
labels - list of 4 values (default [0,0,0,0]) that control whether
meridians are labelled where they intersect the left, right, top or
bottom of the plot. For example labels=[1,0,0,1] will cause meridians
to be labelled where they intersect the left and bottom of the plot,
but not the right and top. Labels are located with a precision of 0.1
degrees and are drawn using mathtext.
font - mathtext font used for labels ('rm','tt','it' or 'cal', default 'rm'.
fontsize - font size in points for labels (default 12).
"""
# don't draw meridians past latmax, always draw parallel at latmax.
latmax = 80. # not used for cyl, merc projections.
# offset for labels.
yoffset = (self.urcrnry-self.llcrnry)/100./self.aspect
xoffset = (self.urcrnrx-self.llcrnrx)/100.
if self.projection not in ['merc','cyl']:
lats = N.arange(-latmax,latmax+1).astype('f')
else:
lats = N.arange(-90,91).astype('f')
xdelta = 0.1*(self.xmax-self.xmin)
ydelta = 0.1*(self.ymax-self.ymin)
for merid in meridians:
lons = merid*N.ones(len(lats),'f')
x,y = self(lons,lats)
# remove points outside domain.
testx = N.logical_and(x>=self.xmin-xdelta,x<=self.xmax+xdelta)
x = N.compress(testx, x)
y = N.compress(testx, y)
testy = N.logical_and(y>=self.ymin-ydelta,y<=self.ymax+ydelta)
x = N.compress(testy, x)
y = N.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:]-x[0:-1])**2
yd = (y[1:]-y[0:-1])**2
dist = N.sqrt(xd+yd)
split = dist > 500000.
if N.sum(split) and self.projection not in ['merc','cyl']:
ind = (N.compress(split,pylab.squeeze(split*N.indices(xd.shape)))+1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x,y in zip(xl,yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,y,linewidth=linewidth,linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
# draw labels for meridians.
# search along edges of map to see if parallels intersect.
# if so, find x,y location of intersection and draw a label there.
if self.projection == 'cyl':
dx = 0.01; dy = 0.01
elif self.projection == 'merc':
dx = 0.01; dy = 1000
else:
dx = 1000; dy = 1000
for dolab,side in zip(labels,['l','r','t','b']):
if not dolab: continue
# for cyl or merc, don't draw meridians on left or right.
if self.projection in ['cyl','merc'] and side in ['l','r']: continue
if side in ['l','r']:
nmax = int((self.ymax-self.ymin)/dy+1)
if self.urcrnry < self.llcrnry:
yy = self.llcrnry-dy*N.arange(nmax)
else:
yy = self.llcrnry+dy*N.arange(nmax)
if side == 'l':
lons,lats = self(self.llcrnrx*N.ones(yy.shape,'f'),yy,inverse=True)
else:
lons,lats = self(self.urcrnrx*N.ones(yy.shape,'f'),yy,inverse=True)
lons = N.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
else:
nmax = int((self.xmax-self.xmin)/dx+1)
if self.urcrnrx < self.llcrnrx:
xx = self.llcrnrx-dx*N.arange(nmax)
else:
xx = self.llcrnrx+dx*N.arange(nmax)
if side == 'b':
lons,lats = self(xx,self.llcrnry*N.ones(xx.shape,'f'),inverse=True)
else:
lons,lats = self(xx,self.urcrnry*N.ones(xx.shape,'f'),inverse=True)
lons = N.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
for lon in meridians:
if lon<0: lon=lon+360.
# find index of meridian (there may be two, so
# search from left and right).
try:
nl = lons.index(int(lon*10))
except:
nl = -1
try:
nr = len(lons)-lons[::-1].index(int(lon*10))-1
except:
nr = -1
if lon>180:
lonlab = r'$\%s{%g\/^{\circ}\/W}$'%(font,N.fabs(lon-360))
elif lon<180 and lon != 0:
lonlab = r'$\%s{%g\/^{\circ}\/E}$'%(font,lon)
else:
lonlab = r'$\%s{%g\/^{\circ}}$'%(font,lon)
# meridians can intersect each map edge twice.
for i,n in enumerate([nl,nr]):
lat = lats[n]/10.
# no meridians > latmax for projections other than merc,cyl.
if self.projection not in ['merc','cyl'] and lat > latmax: continue
# don't bother if close to the first label.
if i and abs(nr-nl) < 100: continue
if n > 0:
if side == 'l':
pylab.text(self.llcrnrx-xoffset,yy[n],lonlab,horizontalalignment='right',verticalalignment='center',fontsize=fontsize)
elif side == 'r':
pylab.text(self.urcrnrx+xoffset,yy[n],lonlab,horizontalalignment='left',verticalalignment='center',fontsize=fontsize)
elif side == 'b':
pylab.text(xx[n],self.llcrnry-yoffset,lonlab,horizontalalignment='center',verticalalignment='top',fontsize=fontsize)
else:
pylab.text(xx[n],self.urcrnry+yoffset,lonlab,horizontalalignment='center',verticalalignment='bottom',fontsize=fontsize)
# make sure axis ticks are turned off
ax.set_xticks([])
ax.set_yticks([])
def drawmeridians(self,ax,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1],labels=[0,0,0,0],\
font='rm',fontsize=12):
"""
draw meridians (longitude lines).
ax - current axis instance.
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
labels - list of 4 values (default [0,0,0,0]) that control whether
meridians are labelled where they intersect the left, right, top or
bottom of the plot. For example labels=[1,0,0,1] will cause meridians
to be labelled where they intersect the left and bottom of the plot,
but not the right and top. Labels are located with a precision of 0.1
degrees and are drawn using mathtext.
font - mathtext font used for labels ('rm','tt','it' or 'cal', default 'rm'.
fontsize - font size in points for labels (default 12).
"""
# don't draw meridians past latmax, always draw parallel at latmax.
latmax = 80. # not used for cyl, merc projections.
# offset for labels.
yoffset = (self.urcrnry - self.llcrnry) / 100. / self.aspect
xoffset = (self.urcrnrx - self.llcrnrx) / 100.
if self.projection not in ['merc', 'cyl']:
lats = N.arange(-latmax, latmax + 1).astype('f')
else:
lats = N.arange(-90, 91).astype('f')
xdelta = 0.1 * (self.xmax - self.xmin)
ydelta = 0.1 * (self.ymax - self.ymin)
for merid in meridians:
lons = merid * N.ones(len(lats), 'f')
x, y = self(lons, lats)
# remove points outside domain.
testx = N.logical_and(x >= self.xmin - xdelta,
x <= self.xmax + xdelta)
x = N.compress(testx, x)
y = N.compress(testx, y)
testy = N.logical_and(y >= self.ymin - ydelta,
y <= self.ymax + ydelta)
x = N.compress(testy, x)
y = N.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:] - x[0:-1])**2
yd = (y[1:] - y[0:-1])**2
dist = N.sqrt(xd + yd)
split = dist > 500000.
if N.sum(split) and self.projection not in ['merc', 'cyl']:
ind = (N.compress(
split, pylab.squeeze(split * N.indices(xd.shape))) +
1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x, y in zip(xl, yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,
y,
linewidth=linewidth,
linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
# draw labels for meridians.
# search along edges of map to see if parallels intersect.
# if so, find x,y location of intersection and draw a label there.
if self.projection == 'cyl':
dx = 0.01
dy = 0.01
elif self.projection == 'merc':
dx = 0.01
dy = 1000
else:
dx = 1000
dy = 1000
for dolab, side in zip(labels, ['l', 'r', 't', 'b']):
if not dolab: continue
# for cyl or merc, don't draw meridians on left or right.
if self.projection in ['cyl', 'merc'] and side in ['l', 'r']:
continue
if side in ['l', 'r']:
nmax = int((self.ymax - self.ymin) / dy + 1)
if self.urcrnry < self.llcrnry:
yy = self.llcrnry - dy * N.arange(nmax)
else:
yy = self.llcrnry + dy * N.arange(nmax)
if side == 'l':
lons, lats = self(self.llcrnrx * N.ones(yy.shape, 'f'),
yy,
inverse=True)
else:
lons, lats = self(self.urcrnrx * N.ones(yy.shape, 'f'),
yy,
inverse=True)
lons = N.where(lons < 0, lons + 360, lons)
lons = [int(lon * 10) for lon in lons.tolist()]
lats = [int(lat * 10) for lat in lats.tolist()]
else:
nmax = int((self.xmax - self.xmin) / dx + 1)
if self.urcrnrx < self.llcrnrx:
xx = self.llcrnrx - dx * N.arange(nmax)
else:
xx = self.llcrnrx + dx * N.arange(nmax)
if side == 'b':
lons, lats = self(xx,
self.llcrnry * N.ones(xx.shape, 'f'),
inverse=True)
else:
lons, lats = self(xx,
self.urcrnry * N.ones(xx.shape, 'f'),
inverse=True)
lons = N.where(lons < 0, lons + 360, lons)
lons = [int(lon * 10) for lon in lons.tolist()]
lats = [int(lat * 10) for lat in lats.tolist()]
for lon in meridians:
if lon < 0: lon = lon + 360.
# find index of meridian (there may be two, so
# search from left and right).
try:
nl = lons.index(int(lon * 10))
except:
nl = -1
try:
nr = len(lons) - lons[::-1].index(int(lon * 10)) - 1
except:
nr = -1
if lon > 180:
lonlab = r'$\%s{%g\/^{\circ}\/W}$' % (font,
N.fabs(lon - 360))
elif lon < 180 and lon != 0:
lonlab = r'$\%s{%g\/^{\circ}\/E}$' % (font, lon)
else:
lonlab = r'$\%s{%g\/^{\circ}}$' % (font, lon)
# meridians can intersect each map edge twice.
for i, n in enumerate([nl, nr]):
lat = lats[n] / 10.
# no meridians > latmax for projections other than merc,cyl.
if self.projection not in ['merc', 'cyl'] and lat > latmax:
continue
# don't bother if close to the first label.
if i and abs(nr - nl) < 100: continue
if n > 0:
if side == 'l':
pylab.text(self.llcrnrx - xoffset,
yy[n],
lonlab,
horizontalalignment='right',
verticalalignment='center',
fontsize=fontsize)
elif side == 'r':
pylab.text(self.urcrnrx + xoffset,
yy[n],
lonlab,
horizontalalignment='left',
verticalalignment='center',
fontsize=fontsize)
elif side == 'b':
pylab.text(xx[n],
self.llcrnry - yoffset,
lonlab,
horizontalalignment='center',
verticalalignment='top',
fontsize=fontsize)
else:
pylab.text(xx[n],
self.urcrnry + yoffset,
lonlab,
horizontalalignment='center',
verticalalignment='bottom',
fontsize=fontsize)
# make sure axis ticks are turned off
ax.set_xticks([])
ax.set_yticks([])
def find_closest(input_array, target_array, tol):
"""
Find the set of elements in input_array that are closest to
elements in target_array. Record the indices of the elements in
target_array that are within tolerance, tol, of their closest
match. Also record the indices of the elements in target_array
that are outside tolerance, tol, of their match.
For example, given an array of observations with irregular
observation times along with an array of times of interest, this
routine can be used to find those observations that are closest to
the times of interest that are within a given time tolerance.
NOTE: input_array must be sorted! The array, target_array, does not have to be sorted.
Inputs:
input_array: a sorted Float64 numarray
target_array: a Float64 numarray
tol: a tolerance
Returns:
closest_indices: the array of indices of elements in input_array that are closest to elements in target_array
accept_indices: the indices of elements in target_array that have a match in input_array within tolerance
reject_indices: the indices of elements in target_array that do not have a match in input_array within tolerance
"""
input_array_len = len(input_array)
closest_indices = numarray.searchsorted(
input_array,
target_array) # determine the locations of target_array in input_array
acc_rej_indices = [-1] * len(target_array)
curr_tol = [tol] * len(target_array)
est_tol = 0.0
for i in xrange(len(target_array)):
best_off = 0 # used to adjust closest_indices[i] for best approximating element in input_array
if closest_indices[i] >= input_array_len:
# the value target_array[i] is >= all elements in input_array so check whether it is within tolerance of the last element
closest_indices[i] = input_array_len - 1
est_tol = target_array[i] - input_array[closest_indices[i]]
if est_tol < curr_tol[i]:
curr_tol[i] = est_tol
acc_rej_indices[i] = i
elif target_array[i] == input_array[closest_indices[i]]:
# target_array[i] is in input_array
est_tol = 0.0
curr_tol[i] = 0.0
acc_rej_indices[i] = i
elif closest_indices[i] == 0:
# target_array[i] is <= all elements in input_array
est_tol = input_array[0] - target_array[i]
if est_tol < curr_tol[i]:
curr_tol[i] = est_tol
acc_rej_indices[i] = i
else:
# target_array[i] is between input_array[closest_indices[i]-1] and input_array[closest_indices[i]]
# and closest_indices[i] must be > 0
top_tol = input_array[closest_indices[i]] - target_array[i]
bot_tol = target_array[i] - input_array[closest_indices[i] - 1]
if bot_tol <= top_tol:
est_tol = bot_tol
best_off = -1 # this is the only place where best_off != 0
else:
est_tol = top_tol
if est_tol < curr_tol[i]:
curr_tol[i] = est_tol
acc_rej_indices[i] = i
if est_tol <= tol:
closest_indices[i] += best_off
accept_indices = numarray.compress(numarray.greater(acc_rej_indices, -1),
acc_rej_indices)
reject_indices = numarray.compress(numarray.equal(acc_rej_indices, -1),
numarray.arange(len(acc_rej_indices)))
return (closest_indices, accept_indices, reject_indices)
To avoid repeated sqrt calculations the discard is based on an
enclosing square.
'''
global lengthRemaining, trial
lengthRemaining+= [[P.shape[0]]]
Pz= P - p # zero based neighbourhood
while len(Pz):
Pt= Pz[0] # trial value
Pta= N.abs(Pt)
Pz= Pz[1:]
pd= math.sqrt(N.dot(Pta, Pta)) # distance of p from the trial
value
goodCases= N.logical_and((Pz < pd),
(Pz > -pd))# use the enclosing square
goodCases= N.logical_and.reduce(goodCases, 1)
Pz= N.compress(goodCases, Pz) # discard points outside the square
if len(Pz) == 1:
Pt= Pz[0] # We have found the closest
Pz= []
lengthRemaining[trial]+= [len(Pz)]
z= 100
trial+= 1
return Pt + p
if __name__ == '__main__':
for sampleSize in range(100, 5000, 100):
P= R.random(shape= (sampleSize, 2))
for i in range(20):
p= R.random((1, 2)) # point
a= find(P, p)
## print 'Closest neighbour:', a[0]
def cullmembers(self):
self.z = N.compress(self.memb > 0, self.z)
self.ra = N.compress(self.memb > 0, self.ra)
self.dec = N.compress(self.memb > 0, self.dec)
self.distBCG = N.compress(self.memb > 0, self.distBCG)
self.distBCGR200 = N.compress(self.memb > 0, self.distBCGR200)
self.dz = N.compress(self.memb > 0, self.dz)
self.o2 = N.compress(self.memb > 0, self.o2)
self.erro2 = N.compress(self.memb > 0, self.erro2)
self.u = N.compress(self.memb > 0, self.u)
self.g = N.compress(self.memb > 0, self.g)
self.r = N.compress(self.memb > 0, self.r)
self.i = N.compress(self.memb > 0, self.i)
self.zm = N.compress(self.memb > 0, self.zm)
self.sf = N.compress(self.memb > 0, self.sf)
self.tot = N.compress(self.memb > 0, self.tot)
self.memb = N.compress(self.memb > 0, self.memb)
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 matchum(file1,
file2,
tol=10,
perr=4,
aerr=1.0,
nmax=40,
im_masks1=[],
im_masks2=[],
debug=0,
domags=0,
xrange=None,
yrange=None,
sigma=4,
aoffset=0):
'''Take the output of two sextractor runs and match up the objects with
each other (find out which objects in the first file match up with
objects in the second file. The routine considers a 'match' to be any
two objects that are closer than tol pixels (after applying the shift).
Returns a 6-tuple: (x1,y1,x2,y2,o1,o2). o1 and o2 are the ojbects
numbers such that o1[i] in file 1 corresponds to o2[i] in file 2.'''
NA = num.NewAxis
sexdata1 = readsex(file1)
sexdata2 = readsex(file2)
# Use the readsex data to get arrays of the (x,y) positions
x1 = num.asarray(sexdata1[0]['X_IMAGE'])
y1 = num.asarray(sexdata1[0]['Y_IMAGE'])
x2 = num.asarray(sexdata2[0]['X_IMAGE'])
y2 = num.asarray(sexdata2[0]['Y_IMAGE'])
m1 = num.asarray(sexdata1[0]['MAG_BEST'])
m2 = num.asarray(sexdata2[0]['MAG_BEST'])
o1 = num.asarray(sexdata1[0]['NUMBER'])
o2 = num.asarray(sexdata2[0]['NUMBER'])
f1 = num.asarray(sexdata1[0]['FLAGS'])
f2 = num.asarray(sexdata2[0]['FLAGS'])
# First, make a cut on the flags:
gids = num.where(f1 < 4)
x1 = x1[gids]
y1 = y1[gids]
m1 = m1[gids]
o1 = o1[gids]
gids = num.where(f2 < 4)
x2 = x2[gids]
y2 = y2[gids]
m2 = m2[gids]
o2 = o2[gids]
# next, if there is a range to use:
if xrange is not None and yrange is not None:
cond = num.greater(x1, xrange[0])*num.less(x1,xrange[1])*\
num.greater(y1, yrange[0])*num.less(y1,yrange[1])
gids = num.where(cond)
x1 = x1[gids]
y1 = y1[gids]
m1 = m1[gids]
o1 = o1[gids]
cond = num.greater(x2, xrange[0])*num.less(x2,xrange[1])*\
num.greater(y2, yrange[0])*num.less(y2,yrange[1])
gids = num.where(cond)
x2 = x2[gids]
y2 = y2[gids]
m2 = m2[gids]
o2 = o2[gids]
# Use the user masks
for m in im_masks1:
print "applying mask (%d,%d,%d,%d)" % tuple(m)
condx = num.less(x1, m[0]) + num.greater(x1, m[1])
condy = num.less(y1, m[2]) + num.greater(y1, m[3])
gids = num.where(condx + condy)
x1 = x1[gids]
y1 = y1[gids]
m1 = m1[gids]
o1 = o1[gids]
for m in im_masks2:
print "applying mask (%d,%d,%d,%d)" % tuple(m)
condx = num.less(x2, m[0]) + num.greater(x2, m[1])
condy = num.less(y2, m[2]) + num.greater(y2, m[3])
gids = num.where(condx + condy)
x2 = x2[gids]
y2 = y2[gids]
m2 = m2[gids]
o2 = o2[gids]
if nmax:
if len(x1) > nmax:
ids = num.argsort(m1)[0:nmax]
x1 = x1[ids]
y1 = y1[ids]
m1 = m1[ids]
o1 = o1[ids]
if len(x2) > nmax:
ids = num.argsort(m2)[0:nmax]
x2 = x2[ids]
y2 = y2[ids]
m2 = m2[ids]
o2 = o2[ids]
if debug:
print "objects in frame 1:"
print o1
print "objects in frame 2:"
print o2
mp = pygplot.MPlot(2, 1, device='/XWIN')
p = pygplot.Plot()
p.point(x1, y1)
[p.label(x1[i], y1[i], "%d" % o1[i]) for i in range(len(x1))]
mp.add(p)
p = pygplot.Plot()
p.point(x2, y2)
[p.label(x2[i], y2[i], "%d" % o2[i]) for i in range(len(x2))]
mp.add(p)
mp.plot()
mp.close()
# Now, we make 2-D arrays of all the differences in x and y between each pair
# of objects. e.g., dx1[n,m] is the delta-x between object n and m in file 1 and
# dy2[n,m] is the y-distance between object n and m in file 2.
dx1 = x1[NA, :] - x1[:, NA]
dx2 = x2[NA, :] - x2[:, NA]
dy1 = y1[NA, :] - y1[:, NA]
dy2 = y2[NA, :] - y2[:, NA]
# Same, but with angles
da1 = num.arctan2(dy1, dx1) * 180 / num.pi
da2 = num.arctan2(dy2, dx2) * 180 / num.pi
# Same, but with absolute distances
ds1 = num.sqrt(num.power(dx1, 2) + num.power(dy1, 2))
ds2 = num.sqrt(num.power(dx2, 2) + num.power(dy2, 2))
# Here's the real magic: this is a matrix of matrices (4-D). Consider 4 objects:
# objects i and j in file 1 and objects m and n in file 2. dx[i,j,m,n] is the
# difference between delta-xs for objects i,j in file 1 and m,n in file 2. If object
# i corresponds to object m and object j corresponds to object n, this should be a small
# number, irregardless of an overall shift in coordinate systems between file 1 and 2.
dx = dx1[::, ::, NA, NA] - dx2[NA, NA, ::, ::]
dy = dy1[::, ::, NA, NA] - dy2[NA, NA, ::, ::]
da = da1[::, ::, NA, NA] - da2[NA, NA, ::, ::] + aoffset
ds = ds1[::, ::, NA, NA] - ds2[NA, NA, ::, ::]
# pick out close pairs.
#use = num.less(dy,perr)*num.less(dx,perr)*num.less(num.abs(da),aerr)
use = num.less(ds, perr) * num.less(num.abs(da), aerr)
use = use.astype(num.Int32)
#use = num.less(num.abs(da),perr)
suse = num.add.reduce(num.add.reduce(use, 3), 1)
print suse[0]
guse = num.greater(suse, suse.flat.max() / 2)
i = [j for j in range(x1.shape[0]) if num.sum(guse[j])]
m = [num.argmax(guse[j]) for j in range(x1.shape[0]) if num.sum(guse[j])]
xx0, yy0, oo0, mm0 = num.take([x1, y1, o1, m1], i, 1)
xx1, yy1, oo1, mm1 = num.take([x2, y2, o2, m2], m, 1)
if debug:
mp = pygplot.MPlot(2, 1, device='/XWIN')
p = pygplot.Plot()
p.point(xx0, yy0)
[p.label(xx0[i], yy0[i], "%d" % oo0[i]) for i in range(len(xx0))]
mp.add(p)
p = pygplot.Plot()
p.point(xx1, yy1)
[p.label(xx1[i], yy1[i], "%d" % oo1[i]) for i in range(len(xx1))]
mp.add(p)
mp.plot()
mp.close()
xshift, xscat = stats.bwt(xx0 - xx1)
xscat = max([1.0, xscat])
yshift, yscat = stats.bwt(yy0 - yy1)
yscat = max([1.0, yscat])
mshift, mscat = stats.bwt(mm0 - mm1)
print "xscat = ", xscat
print "yscat = ", yscat
print "xshift = ", xshift
print "yshift = ", yshift
print "mshift = ", mshift
print "mscat = ", mscat
keep = num.less(num.abs(xx0-xx1-xshift),sigma*xscat)*\
num.less(num.abs(yy0-yy1-yshift),sigma*yscat)
# This is a list of x,y,object# in each file.
xx0, yy0, oo0, xx1, yy1, oo1 = num.compress(keep,
[xx0, yy0, oo0, xx1, yy1, oo1],
1)
if debug:
print file1, oo0
print file2, oo1
mp = pygplot.MPlot(2, 1, device='temp.ps/CPS')
p1 = pygplot.Plot()
p1.point(xx0, yy0, symbol=25, color='red')
for i in range(len(xx0)):
p1.label(xx0[i], yy0[i], " %d" % oo0[i], color='red')
mp.add(p1)
p2 = pygplot.Plot()
p2.point(xx1, yy1, symbol=25, color='green')
for i in range(len(xx1)):
p2.label(xx1[i], yy1[i], " %d" % oo1[i], color='green')
mp.add(p2)
mp.plot()
mp.close()
if domags:
return (xx0, yy0, mm0, xx1, yy1, mm1, mshift, mscat, oo0, oo1)
else:
return (xx0, yy0, xx1, yy1, oo0, oo1)
def stellarmr(self):
pylab.cla()
x=N.compress((self.mpaflag > 0.1),self.Mabs)
y=N.compress((self.mpaflag > 0.1),self.stellarmass)
print "len x,y,Mabs = ",len(x),len(y),len(self.Mabs)
#x=[]
#y=[]
#for i in range(len(self.Mabs)):
# if self.mpaflag[i] > 0.1:
# x.append(self.Mabs[i])
# y.append(self.stellarmass[i])
#print "len x,y,Mabs =",len(x),len(y),len(self.Mabs)
#x=N.array(x,'d')
#y=N.array(y,'d')
y=N.log10(y/1.e11)
xmin=-24.
xmax=-18.
ymin=-3.
ymax=2.
#my.contour(x,y,xmin,xmax,ymin,ymax)
#pylab.plot(x,y,'k.',markersize=.01,zorder=1)
pylab.xlabel(r'$\rm{M_r}$',fontsize=28.)
pylab.ylabel(r'$\rm{log_{10}(M_* / 10^{11} \ M_\odot)}$',fontsize=28.)
#(a,b)=fitpowerlaw(x,y)
xtrans=-21.5
b=-13.8
m=-.63
xl=N.arange(xtrans,-18.,.05)
xl=N.array(xl,'f')
yl=N.zeros(len(xl),'f')
yl=m*(xl)+b
#xl=-1.*xl
#print xl
#print yl
pylab.plot(xl,yl,'b-',lw=4,label='_nolegend_')
m2=-.47
b2=(m-m2)*xtrans+b
print "b2 = ",b2
xl=N.arange(-24.,(xtrans+.05),.05)
xl=N.array(xl,'f')
yl=N.zeros(len(xl),'f')
yl=m2*(xl)+b2
#xl=-1.*xl
#print xl
#print yl
pylab.plot(xl,yl,'c-',lw=4,label='_nolegend_')
pylab.axvline(x=-20.6,linewidth=3,ls='--',c='g')
#(xbin,ybin,ybinerr)=my.binitbinsfix(xl,(xl+0.5),x,y)
#pylab.plot(xbin,ybin,'b-',linewidth=4.)
pylab.hold()
my.contourf(x,y,xmin,xmax,ymin,ymax)
ax=pylab.gca()
#ax.set_yscale('log')
pylab.axis([-24.5,-17.5,-3.,1.2])
pylab.savefig('stellarmr.eps')
