def test_pv_invert_many_plot(rdict):
plt = setuplot()
plt.figure(1)
plt.clf()
plt.hist(rdict['rms'],20)
plt.xlabel(r'Order increase')
plt.ylabel('Number')
plt.title('test')
plt.show()
def dc4_plot_exposures(serun, overplot=True, prompt=False, typ='png', data=None):
import astro_util
plt = setuplot('Agg')
plt.rcParams['figure.figsize'] = 8.5,11
plt.clf()
fpath = deswl.files.wlse_collated_path(serun, 'gal', ftype='rec',
region=[1,2,3,4])
fields=['ra','dec','expname','ccd']
if data is None:
data=esutil.io.read(fpath, verbose=True, fields=fields, norecfile=True)
if overplot:
outdir=os.path.dirname(fpath)
outdir=os.path.join(outdir, 'plots')
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile=os.path.basename(fpath).replace('.rec', '-pos.'+typ)
outfile=os.path.join(outdir, outfile)
stdout.write("Will write image: %s\n" % outfile)
stdout.write("shifting ra\n")
sra=astro_util.shiftra(data['ra'])
stdout.write("Getting ranges\n")
minra=sra.min()
maxra=sra.max()
midra=(maxra+minra)/2.
mindec=data['dec'].min()
maxdec=data['dec'].max()
middec=(maxdec+mindec)/2.
from mpl_toolkits.basemap import Basemap
#map = Basemap(projection='moll', lon_0=0)
map = Basemap(projection='cass',
llcrnrlon=-33, llcrnrlat=mindec,
urcrnrlon=-18, urcrnrlat=-5,
lon_0 = midra, lat_0=middec)
#xrange=map(minra, maxra)
#yrange=map(mindec,maxdec)
#i=raw_input('hit a key: ')
#plt.xlim(xrange[0], xrange[1])
#plt.ylim(yrange[0], yrange[1])
#stdout.write("Writing figure: %s\n" % outfile)
#plt.savefig(outfile, dpi=100, bbox_inches='tight',pad_inches=0.5)
#return
stdout.write('Getting exposure names\n')
enames = numpy_util.unique(data['expname'],values=True)
stdout.write('Plotting exposures\n')
ntot=len(enames)
i=0
for ename in sorted(enames):
stdout.write('%s/%s %s\n' % (i,ntot,ename))
i += 1
w, = numpy.where( data['expname'] == ename )
if w.size > 0:
x, y = map(sra[w], data['dec'][w])
if overplot:
plt.plot( x, y, '.',markersize=2)
else:
plt.clf()
plt.plot( x, y, '.',color='tan',markersize=2)
ax=plt.axes()
plt.text(0.85,0.9,ename,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,fontsize=18)
for ccd in range(1,63):
w2,=numpy.where(data['ccd'][w] == ccd)
if w2.size > 0:
w2=w[w2]
m=x.mean()
my=y.mean()
plt.text( mx, my, str(ccd),
fontsize=14,
weight='extra bold',
horizontalalignment='center',
verticalalignment='center')
if prompt:
x=raw_input('hit a key: ')
if x == 'q':
return
#merid=numpy.arange(0.0, 365., 5.)
#para =numpy.arange(-90., 95., 5.)
merid=numpy.arange(0.0, 365., 5.)
para =numpy.arange(-90., 95., 5.)
merid_label=numpy.ones(merid.size)
para_label=numpy.ones(para.size)
stdout.write("drawing meridians\n")
map.drawmeridians(merid, labels=[0,0,0,1], labelstyle='+/-')#, yoffset=-0.05)
stdout.write("drawing parallels\n")
map.drawparallels(para,labels=[1,0,0,0], labelstyle='+/-')#, xoffset=0.05)
#plt.xlabel('RA')
#plt.ylabel('DEC')
if overplot:
stdout.write("Writing figure: %s\n" % outfile)
plt.savefig(outfile,
dpi=100,
transparent=False,
bbox_inches='tight',pad_inches=0.5)
def plot_shearxy_byccd(serun, region,
example_wcs_byccd=None, outfile=None, typ='pdf'):
"""
Take the input data, split by ccd, and plot each separately. The x,y
are converted to a ra,dec type system that preserved the layout of
the camera. We don't use the actual ra/dec here because we want to
be able to combine multiple exposures on the same plot.
"""
from numpy import arctan2, pi
fpath=deswl.files.wlse_collated_path(serun,'gal',ftype='rec',region=region)
data = esutil.io.read(fpath, verbose=True)
if data.size == 0:
stdout.write("No objects in region: %s\n" % region)
return
outdir=os.path.dirname(fpath)
outdir=os.path.join(outdir, 'plots')
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile=os.path.basename(fpath).replace('.rec', '-shear-byccd.'+typ)
outfile=os.path.join(outdir, outfile)
stdout.write("Will write image: %s\n" % outfile)
plt=setuplot('Agg')
plt.clf()
if example_wcs_byccd is None:
example_wcs_byccd = get_exposure_wcs_example()
h,rev = esutil.stat.histogram(data['ccd'], rev=True, min=1,max=62)
allstats = []
#for ccd in range(1,63):
running_shear1=0.0
running_shear2=0.0
running_shear_weight=0.0
for iccd in range(len(h)):
if rev[iccd] != rev[iccd+1]:
w = rev[ rev[iccd]:rev[iccd+1] ]
ccd = data['ccd'][w[0]]
stdout.write('\tccd=%s\n' % ccd)
# no copy is made here!
stats = stats_shearxy(data[w], nx=3, ny=3)
ccd_wcs = example_wcs_byccd[ccd]
xx,yy = ccd_wcs.image2sky(stats['mx'],stats['my'])
xx -= 337.3
yy += 15.0
stats['mx'] = xx
stats['my'] = yy
allstats.append(stats)
weights = 1.0/(stats['mshear1_err']**2 + stats['mshear2_err']**2)
s1 = stats['mshear1']*weights
s2 = stats['mshear2']*weights
s1sum=s1.sum()
s2sum=s2.sum()
wsum = weights.sum()
running_shear1 += s1sum
running_shear2 += s2sum
running_shear_weight += wsum
thise1 = s1sum/wsum
thise2 = s2sum/wsum
thisangle = 0.5*arctan2(thise2, thise1)*180./pi
stdout.write("\t\t<e1>=%s\n" % thise1 )
stdout.write("\t\t<e2>=%s\n" % thise2 )
stdout.write("\t\t<angle>=%s\n" % thisangle )
#plt.plot(xx,yy,'.',markersize=1)
#plt.show()
stats = numpy_util.combine_arrlist(allstats)
mshear1 = running_shear1/running_shear_weight
mshear2 = running_shear2/running_shear_weight
mangle = 0.5*arctan2(mshear2, mshear1)*180.0/pi
mshear = numpy.sqrt( mshear1**2 + mshear2**2 )
mangle_err = stats['mangle'].std()/numpy.sqrt(stats['mangle'].size)
print "mins:",stats['mshear1'].min(), stats['mshear2'].min(), \
stats['mshear'].min()
print "maxs:",stats['mshear1'].max(), stats['mshear2'].max(), \
stats['mshear'].max()
stdout.write("overall averages: \n\tshear1: "
"%s\n\tshear2: %s\n\tangle: %s\n" % (mshear1, mshear2,mangle))
stdout.write('\tangle error approximately %s\n' % mangle_err)
# x component of the "vector" version
u = stats['mshear']*numpy.cos(stats['mangle'])#*10000
# y component of the "vector" version
v = stats['mshear']*numpy.sin(stats['mangle'])#*10000
# scale=1 means a vector of length 1 will cover essentially
# all the plot. I want them slightly smaller, so I'm using
# scale=1.5
scale=1.5
ax=plt.axes()
from matplotlib.ticker import MultipleLocator as ml
ax.xaxis.set_minor_locator(ml(0.1))
ax.yaxis.set_minor_locator(ml(0.1))
#plt.quiver(stats['mx'], stats['my'], u, v,headwidth=0,
# scale=scale, pivot='middle')
whiskers(plt, stats['mx'], stats['my'], u, v)
xtext=336.0 - 337.3
whiskers(plt, 336.18-337.3, -14.1+15.0, 0.05, 0.0, color='blue')
plt.text(xtext, -14.1+15, "0.05", verticalalignment='center')
ystart=-15.6 + 15
ystep=-0.08
istep = 0
ytext=ystart+istep*ystep
plt.text(xtext, ytext, r"$\langle \gamma_1 \rangle=%0.3f$" % mshear1,
verticalalignment='center')
istep+=1
ytext=ystart+istep*ystep
plt.text(xtext, ytext, r"$\langle \gamma_2 \rangle=%0.3f$" % mshear2,
verticalalignment='center')
istep+=1
ytext=ystart+istep*ystep
plt.text(xtext,ytext,r"$\langle \theta \rangle=%0.2f^{\circ}$" % mangle,
verticalalignment='center')
# plot a whisker representing the average
istep+=1
ytext=ystart+istep*ystep
svec1 = mshear*numpy.cos( mangle*pi/180. )
svec2 = mshear*numpy.sin( mangle*pi/180. )
plt.text(xtext, ytext, r"$\langle \gamma \rangle=%0.3f$" % mshear,
verticalalignment='center')
istep+=1
ytext=ystart+istep*ystep
whiskers(plt, 336.225-337.3, ytext, svec1, svec2, color='red')
label = 'region%s' % region
ax=plt.axes()
plt.text(0.90, 0.9, label,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=18)
# this is so the arrows have the right angle
plt.axis('equal')
plt.ylim(-16.1+15.0,-13.9+15.0)
if outfile is not None:
stdout.write("Writing file: %s\n" % outfile)
plt.savefig(outfile, bbox_inches='tight', pad_inches=0.2)
else:
plt.show()
#print 'mean shear: ',stats['mshear']
return example_wcs_byccd
def test_invert(sip=False, invert=False, distort=True,
dofacrange=False, doorange=False):
"""
Test the inversion routine in wcsutil
"""
indir=os.path.expanduser("~/data/astrometry/image")
plotdir=os.path.join(indir,'plots')
if sip:
imname=os.path.join(indir, 'newheader.fits')
fend = '-sip'
else:
imname=os.path.join(indir, "BCS2304-5436Og.070914_0304.088_04.fits")
fend = '--pv'
catext=2
sys.stdout.write("Image file: %s\n" % imname)
hdr = pyfits.getheader(imname)
wcs = wcsutil.WCS(hdr)
if invert:
if not dofacrange and not doorange:
if sip:
wcs.InvertSipDistortion()
else:
wcs.InvertPVDistortion()
elif dofacrange:
n = 20
rms = numpy.zeros(n, dtype='f8')
facs = list( range(1,n+1) )
i=0
for fac in facs:
rms[i] = wcs.InvertDistortion(fac=fac,verbose=False)
sys.stdout.write('%s\n' % facs)
sys.stdout.write('%s\n' % rms)
plt = setuplot()
plt.clf()
plt.plot(facs,rms,'.')
elif doorange:
fname='test-order-inverse'+fend+'.pdf'
fname=os.path.join(plotdir,fname)
n=6
order_increases= list( range(n) )
rms = numpy.zeros(n, dtype='f8')
i=0
for oi in order_increases:
rms[i] = wcs.InvertDistortion(order_increase=oi,verbose=False)
i += 1
sys.stdout.write('order increases: %s\n' % order_increases)
sys.stdout.write('rms: %s\n' % rms)
plt = setuplot()
plt.clf()
plt.plot(order_increases, rms,'-.')
#plt.xlabel(r'$\mathrm{order increase}$')
#plt.ylabel('$\mathrm{rms}~[pix]$')
#plt.rc('text',usetex=True)
#plt.rc('font',**{'family':'sans-serif','sans-serif':['computer modern roman']})
#plt.rc('font',**{'family':'serif','sans-serif':['Times New Roman']})
from matplotlib.ticker import MultipleLocator as ml
ax = plt.axes()
ax.xaxis.set_minor_locator(ml(0.1))
ax.yaxis.set_minor_locator(ml(0.1))
plt.xlabel(r'Order increase')
plt.ylabel(r'rms $[pix]$')
ax.set_yscale('log')
sys.stdout.write('Saving figure: %s\n' % fname)
plt.savefig(fname)
fname=fname.replace('.pdf','.eps')
sys.stdout.write('Saving figure: %s\n' % fname)
plt.savefig(fname)
fname=fname.replace('.eps','.ps')
sys.stdout.write('Saving figure: %s\n' % fname)
plt.savefig(fname)
else:
x = numpy.array([21.34, 1000.0, 1500.17], dtype='f8')
y = numpy.array([11.21, 1000.0, 1113.92], dtype='f8')
#wcs.InvertDistortion(order_increase=1)
ra,dec = wcs.image2sky(x,y)
xp,yp = wcs.sky2image(ra,dec, find=True)
sys.stdout.write('x,y= %s\n' % (x,y) )
sys.stdout.write('crval= %s\n' % wcs.crval)
sys.stdout.write('predicted ra,dec= %s\n' % (ra,dec))
sys.stdout.write('inverted xp,yp= %s\n' % (xp,yp))
sys.stdout.write('difference= %s\n' % (x-xp,y-yp))
sys.stdout.write('fractional difference= %s\n' % ( (x-xp)/x,(y-yp)/y ) )
