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singles.py
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singles.py
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#! /usr/bin/env python
from __future__ import print_function
if __name__ == '__main__':
import matplotlib
matplotlib.use('Agg')
import os
import sys
import re
from tempfile import *
import colorsys
import numpy as np
from collections import Counter
from astrom_common import *
from astrom_intra import intrabrickshift
from astrom_inter import findAffine
from astrometry.util.file import *
from astrometry.util.fits import *
from astrometry.util.util import *
from astrometry.util.miscutils import *
#from astrometry.blind.plotstuff import *
from astrometry.util.multiproc import multiproc
def find_overlaps(outlines):
'''
outlines: Outlines of the images in RA,Dec
Returns: overlaps, areas
overlaps: 2-d numpy array, overlaps[i,j] is the fraction of points in i
that are also in j.
areas: approximate area of each image, in square degrees
'''
N = len(outlines)
areas = np.zeros(N)
overlaps = np.zeros((N,N))
for i,out in enumerate(outlines):
areas[i] = polygon_area(out)
outlines2 = [np.array(list(zip(*out))) for out in outlines]
for i,out1 in enumerate(outlines):
for j,out2 in enumerate(outlines):
if j <= i:
continue
rr1,dd1 = out1
rr2,dd2 = out2
if min(rr1) > max(rr2) or min(rr2) > max(rr1):
continue
if min(dd1) > max(dd2) or min(dd2) > max(dd1):
continue
outA,outB = outlines2[i], outlines2[j]
if not polygons_intersect(outA, outB):
continue
cp = clip_polygon(outA, outB)
if len(cp) == 0:
continue
cp = np.array(cp)
area = polygon_area((cp[:,0], cp[:,1]))
overlaps[i,j] = area / areas[i]
overlaps[j,i] = area / areas[j]
return overlaps, areas
def wavelength(f):
'''
Maps a string "F110W" to wavelength in int nanometers: 1100
'''
fmap = { 110:1100, 160:1600 }
# trim F...W
if not ((f[0] in ['F','f']) and (f[-1] in ['w','W','N','n'])):
print('WARNING: wavelength("%s"): expected F###W' % f)
f = int(f[1:-1])
f = fmap.get(f, f)
return f
def argsort_filters(filters):
# Reorder them by wavelength...
nf = []
for f in filters:
if f == 'ref':
nf.append(0.)
else:
nf.append(wavelength(f))
I = np.argsort(nf)
return I
def filters_legend(lp, filters): #, **kwa):
I = argsort_filters(filters)
#plt.legend([lp[i] for i in I], [filters[i] for i in I], **kwa)
return [lp[i] for i in I], [filters[i] for i in I]
def _alfunc(args):
(Ti, Tj, R, i, j) = args
print('Matching', i, 'to', j)
M = Match(Ti, Tj, R)
if len(M.I) == 0:
return None
A = Alignment(Ti, Tj, R, cutrange=R, match=M)
if A.shift() is None:
return None
return A
def readfltgst(fltfn, gstfn, wcsexts):
info = parse_flt_filename(fltfn)
chip = info.get('chip')
filt = info.get('filt')
filt = filt.upper()
name = info.get('name')
hdr = read_header_as_dict(fltfn, 0)
exptime = hdr['EXPTIME']
if chip:
cname = '%s_%i' % (name,chip)
else:
cname = name
wcs = None
for ext in wcsexts:
try:
wcs = Tan(fltfn, ext)
break
except:
print('Failed to read WCS header from extension', ext, 'of', fltfn)
#import traceback
#traceback.print_exc()
print('Read WCS header from', fltfn)
outline = getwcsoutline(wcs)
try:
T = fits_table(gstfn)
print('Read gst file', gstfn, '->', len(T), 'stars')
except:
print('WARNING: failed to read FITS file', gstfn)
import traceback
traceback.print_exc()
return None
cols = T.get_columns()
cols = [c.lower() for c in cols]
#print 'Columns:', cols
if 'mag1_acs' in cols:
magnm = 'mag1_acs'
elif 'mag1_uvis' in cols:
magnm = 'mag1_uvis'
elif 'mag1_ir' in cols:
magnm = 'mag1_ir'
elif 'mag1_wfpc2' in cols:
magnm = 'mag1_wfpc2'
else:
assert(False)
T.magnm = magnm
T.mag = T.get(magnm)
return T, outline, (chip, filt, name, cname, exptime)
def readfltgsts(fltfns, gstfns, wcsexts, Nkeep, Nuniform):
TT = []
outlines = []
chips = []
names = []
cnames = []
filts = []
exptimes = []
Nall = []
for fltfn,gstfn in zip(fltfns, gstfns):
print('gst', gstfn)
print('flt', fltfn)
T, outline, meta = readfltgst(fltfn, gstfn, wcsexts)
(chip, filt, name, cname, exptime) = meta
if Nkeep and len(T) < Nkeep:
print('WARNING: gst file', gstfn, 'contains "only"', len(T), 'stars')
print('outline in RA,Dec:', outline)
rr,dd = outline
ra,dec = np.mean(rr), np.mean(dd)
# check for clockwise polygons
dra = rr - ra
ddec = dd - dec
crossprod = dra[0] * ddec[1] - dra[1] * ddec[0]
if crossprod > 0:
print('counter-clockwise outline -- reversing')
outline = np.array(list(reversed(rr))), np.array(list(reversed(dd)))
Nall.append(len(T))
if Nuniform:
xmax = T.x.max()
nx = int(np.round(xmax / Nuniform))
dx = xmax / nx
ymax = T.y.max()
ny = int(np.round(ymax / Nuniform))
dy = ymax / ny
Nper = int(Nkeep / (nx*ny))
print('Uniform: xymax (%.1f,%.1f)' % (xmax,ymax), 'nx,ny', nx,ny,
'nkeep', Nkeep, ', n stars per bin:', Nper)
xbin = (T.x / dx).astype(int).clip(0, nx-1)
ybin = (T.y / dy).astype(int).clip(0, ny-1)
II = []
for y in range(ny):
I = np.flatnonzero(ybin == y)
xb = xbin[I]
for x in range(nx):
J = np.flatnonzero(xb == x)
K = I[J]
KK = np.argsort(T.mag[K])
Ithis = K[KK[:Nper]]
if len(Ithis) < Nper:
Ithis = np.append(Ithis, np.zeros(Nper - len(Ithis), int) - 1)
II.append(Ithis)
# We stack them in this way so that the index is sorted to have the
# brightest star per cell first, then the second-brightest, etc.
I = np.vstack(II).T.ravel()
T.cut(I[ I>=0 ])
elif Nkeep:
T.cut(np.argsort(T.mag)[:Nkeep])
TT.append(T)
filts.append(filt)
chips.append(chip)
names.append(name)
cnames.append(cname)
exptimes.append(exptime)
outlines.append(outline)
filts = np.array(filts)
names = np.array(names)
cnames = np.array(cnames)
chips = np.array(chips)
exptimes = np.array(exptimes)
r0 = min([T.ra.min() for T in TT])
r1 = max([T.ra.max() for T in TT])
d0 = min([T.dec.min() for T in TT])
d1 = max([T.dec.max() for T in TT])
print('Read', len(TT), 'fields:')
for i,(cn,N,filt,t) in enumerate(zip(cnames,Nall,filts,exptimes)):
print(' ', i+1, cn, filt, 'exposure %.1f sec' % t, N, 'stars')
return (TT, outlines,
(chips, names, cnames, filts, exptimes, Nall, (r0,r1,d0,d1)),
)
def alignment_plots(afffn, name, Nkeep, Nuniform, R, minoverlap,
perfield, nocache, mp, wcsexts,
tables=None,
lexsort=True, reffn=None, refrad=0.5,
cutfunction=None):
import pylab as plt
from astrometry.util.plotutils import (
PlotSequence, loghist, plothist, setRadecAxes)
Taff = fits_table(afffn)
affs = Affine.fromTable(Taff)
if tables is None:
# Trim extra spaces off of filenames (spaces added by some FITS readers)
Taff.flt = np.array([s.strip() for s in Taff.flt])
Taff.gst = np.array([s.strip() for s in Taff.gst])
TT, outlines, meta = readfltgsts(Taff.flt, Taff.gst, wcsexts, Nkeep,
Nuniform)
else:
TT, outlines, meta = tables
if cutfunction is not None:
TT,outlines,meta = cutfunction(TT, outlines, meta)
(chips, names, cnames, filts, exptimes, Nall, rd) = meta
r0,r1,d0,d1 = rd
for aff,T in zip(affs,TT):
T.ra,T.dec = aff.apply(T.ra, T.dec)
Tref = None
if reffn:
print('Reading reference catalog', reffn)
Tref = fits_table(reffn)
print('Got', len(Tref))
print('Cutting to RA,Dec range', r0,r1,d0,d1)
Tref.cut((Tref.ra > r0) * (Tref.ra < r1) *
(Tref.dec > d0) * (Tref.dec < d1))
print('Cut to', len(Tref))
#ikwargs.update(ref=Tref, refrad=refrad)
nstars = np.array([len(T) for T in TT])
wavelengths = np.array([wavelength(f) for f in filts])
# Reorder...
if lexsort:
I = np.lexsort((names, -exptimes, chips, wavelengths))
# Re-sort
affs = [affs[i] for i in I]
outlines = [outlines[i] for i in I]
TT = [TT[i] for i in I]
filts = filts[I]
names = names[I]
chips = chips[I]
exptimes = exptimes[I]
nstars = nstars[I]
cnames = cnames[I]
# arrays that we're not going to bother re-sorting
del wavelengths
uf = np.unique(filts)
uf = uf[argsort_filters(uf)]
print('(sorted) Filters:', uf)
print('Exposure times:', np.unique(exptimes))
cc,ss,fcmap,fsmap = get_symbols_for_filts(uf)
fcmap['ref'] = 'k'
fsmap['ref'] = '.'
N = len(TT)
OO,areas = find_overlaps(outlines)
summary = not perfield
ps1 = PlotSequence(name + '-summary', format='%02i')
ps = ps1
# Map
lp = {}
plt.clf()
for (r,d),chip,filt in zip(outlines, chips, filts):
p1 = plt.plot(r, d, '-', color=fcmap[filt], alpha=0.5, lw=2)
if not filt in lp:
lp[filt] = p1[0]
#plt.text(np.mean(r), np.mean(d), '%i'%chip)
setRadecAxes(r0,r1,d0,d1)
plt.legend([lp[f] for f in uf], uf)
ps.savefig()
eepfn = name+'-ee.pickle'
if summary and os.path.exists(eepfn) and (not nocache):
print('Reading cache file', eepfn)
EE = unpickle_from_file(eepfn)
else:
EE = []
ps = PlotSequence(name + '-perimage', format='%03i')
for i,Ti in enumerate(TT):
AA = []
IJ = []
args = []
for j,Tj in enumerate(TT):
if i == j:
if Tref:
args.append((Ti, Tref, refrad, i, i))
# So that the reference field always appears first in the
# overlap-sorted list.
OO[i,i] = 1.0
continue
if summary and j < i:
continue
if OO[i,j] <= minoverlap:
print('Overlap between fields', i, 'and', j, 'is', OO[i,j], '-- not trying to match them')
continue
args.append((Ti, Tj, R, i, j))
MR = mp.map(_alfunc, args)
goodj = []
for A,a in zip(MR,args):
if A is None:
continue
j = a[-1]
AA.append(A)
IJ.append((i,j))
goodj.append(j)
for (ii,j),A in zip(IJ,AA):
esize = np.sqrt(np.product(A.getEllipseSize()))
x,y = A.getContours()
EE.append((x,y,i,j, len(A.match.I), esize))
if summary:
continue
iname = os.path.basename(Taff.gst[i]).replace('.gst.fits', '')
N = len(AA)
cols = int(np.ceil(np.sqrt(len(AA))))
rows = int(np.ceil(N / float(cols)))
plt.clf()
plt.subplots_adjust(hspace=0.01, wspace=0.01,
left=0.1, right=0.96,
bottom=0.1, top=0.90)
if False:
plt.clf()
plothist(Ti.ra, Ti.dec, 200, range=((r0,r1),(d0,d1)),
imshowargs=dict(cmap=antigray), dohot=False, docolorbar=False)
lp = {}
for j,(r,d) in enumerate(outlines):
if j in goodj:
filt = filts[j]
p1 = plt.plot(r,d,'-', color=fcmap[filt], lw=2, alpha=0.5)
if not filt in lp:
lp[filt] = p1[0]
else:
plt.plot(r,d,'-', color='0.5', lw=2, alpha=0.5)
# plot bad ones on top
for j,(r,d) in enumerate(outlines):
if not j in goodj:
plt.plot(r,d,'-', color='0.5', lw=2, alpha=0.5)
setRadecAxes(r0,r1,d0,d1)
plt.legend([lp[f] for f in uf], uf)
plt.suptitle('%s: %s matched fields' % (name, iname))
ps.savefig()
if False:
plt.clf()
lp = {}
for (ii,j),A in zip(IJ,AA):
filt = filts[j]
x,y = A.getContours()
p1 = plt.plot(x*1000., y*1000., '-', color=fcmap[filt], lw=2, alpha=0.5)
if not filt in lp:
lp[filt] = p1[0]
esize = np.sqrt(np.product(A.getEllipseSize()))
EE.append((x,y,i,j, len(A.match.I), esize))
plt.legend([lp[f] for f in uf], uf)
plt.suptitle('%s: %s alignments' % (name, iname))
plt.axhline(0, color='k', alpha=0.5)
plt.axvline(0, color='k', alpha=0.5)
plt.xlabel('dRA (mas)')
plt.ylabel('dDec (mas)')
ps.savefig()
# Sort by overlap
K = np.argsort([-OO[i,j] for ii,j in IJ])
plt.clf()
nbins = 50
#for j,A in enumerate(AA):
for ik,k in enumerate(K):
ii,j = IJ[k]
A = AA[k]
plt.subplot(rows, cols, 1+ik)
if ii == j:
RR = refrad * 1000.
cname = 'Ref'
filt = ''
btxt = None
else:
RR = R*1000.
cname = cnames[j]
filt = filts[j]
btxt = '%2i %%' % (int(OO[i,j] * 100.))
plotalignment(A, nbins=nbins, rng=[(-RR,RR)]*2,
doclf=False, docolorbar=False,
docutcircle=False)
plt.title('')
ax = plt.axis()
plt.axhline(0, color='b', alpha=0.5)
plt.axvline(0, color='b', alpha=0.5)
if j != ((rows-1)*cols):
plt.xlabel('')
plt.ylabel('')
plt.xticks([])
plt.yticks([])
plt.axis(ax)
plt.text(RR, RR, '%s %s' % (cname, filt),
va='top', color='y', ha='right')
if btxt:
plt.text(-RR,-RR, btxt, va='bottom', ha='left', color='y')
plt.suptitle('%s: %s' % (name, iname))
ps.savefig()
if False:
plt.clf()
for k,(A,(i,j)) in enumerate(zip(AA, IJ)):
plt.subplot(rows, cols, 1+k)
RR = R*1000.
M = A.match
Ti = TT[i]
Tj = TT[j]
loghist(Ti.get(Ti.magnm)[M.I], Tj.get(Tj.magnm)[M.J], nbins,
doclf=False, docolorbar=False)
plt.xticks([])
plt.yticks([])
plt.suptitle('%s: %s: mag-mags of matches' % (name, iname))
ps.savefig()
pickle_to_file(EE, eepfn)
plt.subplots_adjust(hspace=0.25, wspace=0.25,
left=0.1, right=0.96,
bottom=0.1, top=0.90)
N = len(TT)
ngrid = np.zeros((N,N))
esgrid = np.zeros((N,N))
kc = 0
ffmap = {}
lp = []
lt = []
for x,y,i,j,n,esize in EE:
ngrid[j,i] = n
esgrid[i,j] = esize
if i > j:
continue
ngrid[i,j] = n
esgrid[j,i] = esize
if False:
# All-filter-pairs ellipses -- too busy
plt.clf()
for x,y,i,j,n,esize in EE:
filt1 = filts[i]
filt2 = filts[j]
c = ffmap.get((filt1,filt2), None)
keeplp = False
if c is None:
c = cc[kc % len(cc)]
ffmap[(filt1,filt2)] = c
ffmap[(filt2,filt1)] = c
kc += 1
keeplp = True
lt.append('%s-%s' % (filt1,filt2))
p1 = plt.plot(x*1000, y*1000, '-', color=c, alpha=0.1, lw=2)
if keeplp:
lp.append(p1[0])
I = np.argsort(lt)
plt.legend([lp[i] for i in I], [lt[i] for i in I])
plt.axis([-25,25,-25,25])
plt.axis('equal')
ps.savefig()
ps = ps1
plt.clf()
plt.imshow(np.minimum(OO, 1.), interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.title('Fraction of spatial overlap between image pairs')
plt.xticks(np.arange(N), cnames, rotation=90, fontsize=8)
plt.yticks(np.arange(N), cnames, fontsize=8)
ps.savefig()
navailgrid = np.minimum(*np.meshgrid(nstars,nstars))
fgrid = ngrid / navailgrid
dgrid_valid = np.logical_and((navailgrid * OO) > 0, OO > minoverlap)
dgrid = np.zeros_like(fgrid)
K = np.flatnonzero(dgrid_valid)
dgrid.flat[K] = (ngrid.flat[K] / (navailgrid * OO).flat[K])
plt.clf()
plt.imshow(fgrid, interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.title('Fraction of matches between image pairs')
plt.xticks(np.arange(N), cnames, rotation=90, fontsize=8)
plt.yticks(np.arange(N), cnames, fontsize=8)
ps.savefig()
plt.clf()
plt.imshow(dgrid, interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.title('Density of matches between image pairs')
plt.xticks(np.arange(N), cnames, rotation=90, fontsize=8)
plt.yticks(np.arange(N), cnames, fontsize=8)
ps.savefig()
# plt.clf()
# plt.plot(OO.ravel(), ngrid.ravel(), 'r.')
# plt.xlabel('Percent overlap')
# plt.ylabel('Number of matches')
# ps.savefig()
#
# plt.clf()
# plt.plot(OO.ravel(), esgrid.ravel(), 'r.')
# plt.xlabel('Percent overlap')
# plt.ylabel('Match ellipse size')
# ps.savefig()
for olo,ohi in [(None,None), (0.25,None), (0.1, 0.25), (None,0.1)]:
plt.clf()
NF = len(uf)
cols = int(np.ceil(np.sqrt(NF)))
rows = int(np.ceil(NF / float(cols)))
lp = {}
for ii,f1 in enumerate(uf):
I = np.array([(f == f1) for f in filts])
# i is ee[2], j is ee[3]
# Matching ellipses involving filter f1
EEi = [ee for ee in EE if I[ee[2]] or I[ee[3]]]
plt.subplot(rows, cols, ii+1)
for f2 in uf:
J = np.array([(f == f2) for f in filts])
for x,y,i,j,n,esize in EEi:
# Find matching ellipses between filters f1 and f2
if not ((J[j] and I[i]) or (J[i] and I[j])):
continue
# Check overlap fractions
if J[j] and I[i]:
ii,jj = i,j
else:
ii,jj, = j,i
if ii == jj:
OO[ii,jj] = 1.0
#print 'fields', ii,jj, '-> overlap', OO[ii,jj]
if olo and OO[ii,jj] < olo:
continue
if ohi and OO[ii,jj] > ohi:
continue
f2now = f2
if ii == jj:
f2now = 'ref'
iname = os.path.basename(Taff.gst[i]).replace('.gst.fits','')
jname = os.path.basename(Taff.gst[j]).replace('.gst.fits','')
print('Filters', f1, f2, 'images', iname, jname, ': %.1f mas' % (esize*1000.))
c = fcmap[f2now]
p1 = plt.plot(x*1000, y*1000, '-', color=c, alpha=0.1, lw=2)
if not f2now in lp:
p1 = plt.plot([], [], '-', color=c, lw=2)
lp[f2now] = p1[0]
RR = 20
#plt.axis([-RR,RR,-RR,RR])
#plt.axis('scaled')
#plt.axis([-RR,RR,-RR,RR])
ax = plt.axis()
plt.axis('scaled')
M = max([np.abs(a) for a in ax])
plt.axis([-M,M,-M,M])
plt.title(f1 + ' match ellipses')
I = argsort_filters(lp.keys())
vals = list(lp.values())
keys = list(lp.keys())
plt.figlegend([vals[i] for i in I], [keys[i] for i in I], 'upper right')
ostr = ''
if olo and ohi:
ostr = ', overlap [%i %%, %i %%]' % (int(olo * 100.), int(ohi * 100.))
elif olo:
ostr = ', overlap > %i %%' % (int(olo * 100.))
elif ohi:
ostr = ', overlap < %i %%' % (int(ohi * 100.))
plt.suptitle('%s: match ellipses%s' % (name,ostr))
ps.savefig()
plt.clf()
nbad = np.zeros(N)
ntotal = np.zeros(N)
for i,f1 in enumerate(uf):
I = (filts == f1)
plt.subplot(len(uf), 1, i+1)
A = areas[I]
print('Areas:', A)
A = np.mean(A)
print('Mean area in sq deg:', A)
A *= (3600.**2)
fprate = (np.pi * R**2) / A
print('False positive match rate (single star):', fprate)
# There are Nkeep "targets" and Nkeep "darts"
Nfp = fprate * (Nkeep**2)
print('Expected number of false positives:', Nfp)
lp,lt = [],[]
for f2 in uf:
J = (filts == f2)
ob,fb,obr,fbr = [],[],[],[]
for ii in np.flatnonzero(I):
for jj in np.flatnonzero(J):
if ii == jj:
obr.append(OO[ii,jj])
fbr.append(fgrid[ii,jj])
else:
ob.append(OO[ii,jj])
fb.append(fgrid[ii,jj])
cc = fcmap['ref']
p1 = plt.plot(np.array(obr) * 100., np.clip(fbr, 0, 1.),
fsmap['ref'], color=cc, mec=cc, mfc='none')
if i == 0:
lp.append(p1[0])
lt.append('ref')
cc = fcmap[f2]
p1 = plt.plot(np.array(ob) * 100., np.clip(fb, 0., 1.),
fsmap[f2], color=cc, mec=cc, mfc='none')
lp.append(p1[0])
lt.append(f2)
# OLD
J = (filts == f2)
oblock = OO [I,:][:,J]
fblock = fgrid[I,:][:,J]
# cc = fcmap[f2]
# p1 = plt.plot(oblock.ravel() * 100., fblock.ravel(),
# fsmap[f2], color=cc, mec=cc, mfc='none')
# lp.append(p1[0])
# lt.append(f2)
nblock = ngrid[I,:][:,J]
K = np.flatnonzero(oblock > 0.1)
if len(K):
# print smallest nblock / (Nfp*oblock) for K
factorfp = nblock.flat[K] / (Nfp * oblock.flat[K])
B = np.argsort(factorfp)
print('Filters', f1, '--', f2)
print('Number of matches as factor of FP rate:', factorfp[B])
iy,ix = np.unravel_index(K[factorfp < 10.], oblock.shape)
iy = np.flatnonzero(I)[iy]
ix = np.flatnonzero(J)[ix]
for a,b in zip(iy,ix):
print(' ', cnames[a], '--', cnames[b])
print(' ', filts[a], filts[b])
nbad[a] += 1
nbad[b] += 1
iy,ix = np.unravel_index(K, oblock.shape)
iy = np.flatnonzero(I)[iy]
ix = np.flatnonzero(J)[ix]
for a,b in zip(iy,ix):
ntotal[a] += 1
ntotal[b] += 1
plt.ylabel('Fraction of matches')
if i == 0:
plt.figlegend(lp, lt, 'upper left')
ymax = plt.axis()[3]
plt.plot([0, 100.], [0, 1.0], 'k-', alpha=0.5)
plt.plot([0, 100.], [0, 0.5], 'k-', alpha=0.5)
plt.plot([0, 100.], [0, Nfp/Nkeep], 'r--', alpha=0.5)
plt.axhline(0, color='k', alpha=0.5)
plt.axvline(minoverlap * 100, color='k', alpha=0.5)
plt.axvline(0, color='k', alpha=0.5)
plt.xlim(-3, 103)
plt.title(f1)
plt.ylim(-0.03 * ymax, ymax)
plt.xlabel('Percent overlap')
plt.suptitle('%s: fraction of matches' % name)
ps.savefig()
# retrofit
#I = np.arange(len(nstars))
#dgrid_valid[I,I] = True
plt.clf()
for i,f1 in enumerate(uf):
I = (filts == f1)
plt.subplot(len(uf), 1, i+1)
lp,lt = [],[]
for f2 in uf:
J = (filts == f2)
ob,db,obr,dbr = [],[],[],[]
for ii in np.flatnonzero(I):
for jj in np.flatnonzero(J):
if not dgrid_valid[ii,jj]:
#if ii == jj:
# #print 'dgrid invalid: navailgrid', navailgrid[ii,jj],
# #print 'OO', OO[ii,jj]
continue
if ii == jj:
obr.append(OO[ii,jj])
dbr.append(dgrid[ii,jj])
else:
ob.append(OO[ii,jj])
db.append(dgrid[ii,jj])
cc = fcmap['ref']
p1 = plt.plot(np.array(obr) * 100., np.clip(dbr, 0.,1.),
fsmap['ref'], color=cc, mec=cc, mfc='none')
if i == 0:
lp.append(p1[0])
lt.append('ref')
cc = fcmap[f2]
p1 = plt.plot(np.array(ob) * 100., db,
fsmap[f2], color=cc, mec=cc, mfc='none')
lp.append(p1[0])
lt.append(f2)
# oblock = OO [I,:][:,J]
# dblock = dgrid[I,:][:,J]
# K = np.flatnonzero(dgrid_valid[I,:][:,J])
# cc = fcmap[f2]
# p1 = plt.plot(oblock.flat[K] * 100., dblock.flat[K],
# fsmap[f2], color=cc, mec=cc, mfc='none')
# lp.append(p1[0])
plt.ylabel('Density of matches')
if i == 0:
plt.figlegend(lp, lt, 'upper right')
ymax = plt.axis()[3]
plt.axhline(1.0, color='k', alpha=0.5)
plt.axhline(0.5, color='k', alpha=0.5)
plt.axhline(Nfp/Nkeep, color='r', alpha=0.5)
plt.axhline(0, color='k', alpha=0.5)
plt.axvline(minoverlap * 100, color='k', alpha=0.5)
plt.axvline(0, color='k', alpha=0.5)
plt.xlim(-3, 103)
plt.title(f1)
plt.ylim(-0.03 * ymax, ymax)
plt.xlabel('Percent overlap')
plt.suptitle('%s: density of matches' % name)
ps.savefig()
# Worst fields:
fbad = nbad / ntotal
I = np.argsort(-fbad)
for i in I:
print(cnames[i], filts[i], '%.2f bad' % fbad[i], '(%i / %i)' % (nbad[i], ntotal[i]))
plt.clf()
for i,f1 in enumerate(uf):
I = (filts == f1)
plt.subplot(len(uf), 1, i+1)
lp,lt = [],[]
for f2 in uf:
J = (filts == f2)
ob,eb,obr,ebr = [],[],[],[]
for ii in np.flatnonzero(I):
for jj in np.flatnonzero(J):
if ii == jj:
obr.append(OO[ii,jj])
ebr.append(esgrid[ii,jj])
else:
ob.append(OO[ii,jj])
eb.append(esgrid[ii,jj])
cc = fcmap['ref']
p1 = plt.plot(np.array(obr) * 100., ebr,
fsmap['ref'], color=cc, mec=cc, mfc='none')
if i == 0:
lp.append(p1[0])
lt.append('ref')
cc = fcmap[f2]
p1 = plt.plot(np.array(ob) * 100., eb,
fsmap[f2], color=cc, mec=cc, mfc='none')
lp.append(p1[0])
lt.append(f2)
# oblock = OO [I,:][:,J]
# eblock = esgrid[I,:][:,J]
# cc = fcmap[f2]
# p1 = plt.plot(oblock.ravel() * 100., eblock.ravel() * 1000.,
# fsmap[f2], color=cc, mec=cc, mfc='none')
# lp.append(p1[0])
plt.ylabel('Matching ellipse size (mas)')
if i == 0:
plt.figlegend(lp, lt, 'upper right')
plt.axvline(minoverlap * 100, color='k', alpha=0.5)
plt.axvline(0, color='k', alpha=0.5)
plt.xlim(-3, 103)
plt.title(f1)
ymax = plt.axis()[3]
plt.ylim(-0.03 * ymax, ymax)
plt.xlabel('Percent overlap')
plt.suptitle('%s: match ellipse sizes' % name)
ps.savefig()
# Density of matches vs exposure times
plt.clf()
axes = []
for i,f1 in enumerate(uf):
I = (filts == f1)
plt.subplot(len(uf), 1, i+1)
lp,lt = [],[]
for f2 in uf:
J = (filts == f2)
dblock = dgrid[I,:][:,J]
cc = fcmap[f2]
e2 = np.outer(exptimes[I], exptimes[J])
# don't plot points that have zero overlap.
K = np.flatnonzero(dgrid_valid[I,:][:,J])
p1 = plt.semilogx(e2.flat[K], dblock.flat[K],
fsmap[f2], color=cc, mec=cc, mfc='none')
lp.append(p1[0])
plt.ylabel('Density of matches')
if i == 0:
#plt.legend(lp, uf, loc='upper left')
plt.figlegend(lp, uf, 'upper right')
#plt.xlim(-1, 101)
plt.title(f1)
plt.axhline(0, color='k', alpha=0.5)
ymax = plt.axis()[3]
plt.ylim(-0.03 * ymax, ymax)
axes.append(plt.axis())
# Expand axes to common range
axes = np.array(axes)
ax0 = np.max(axes, axis=0)
ax1 = np.min(axes, axis=0)
ax = [ax0[0], ax1[1], ax0[2], ax1[3]]
for i,f1 in enumerate(uf):
plt.subplot(len(uf), 1, i+1)
plt.axis(ax)
plt.xlabel('Exposure time * Exposure time (s^2)')
plt.suptitle('%s: matches vs exposure time' % name)
ps.savefig()
## By filter and exposure time, plot match fractions.
I = np.arange(len(TT))
# how many exposures of each filter are there?
cnt = Counter()
for f in filts:
cnt[f] += 1
if False:
plt.clf()
left = 0
n = 0
k0 = 0
lastfilt = None
subplot = 1
lp = {}
axes = []
xt = []
for k,i in enumerate(I):
f1 = filts[i]
lastone = (k == (len(I)-1)) or (filts[I[k+1]] != f1)
if f1 != lastfilt:
# in subplots per filter?
plt.subplot(len(uf), 1, subplot)
subplot += 1
left = 0
xt = []
k0 = k
lastfilt = f1
n = cnt[f1]
#plt.gca().add_artist(Rectangle((left-0.4, 0), n-0.2, 1.,
# color=fcmap[f1], alpha=0.2, zorder=10))
#left += n
for f2 in uf:
J = np.flatnonzero(filts == f2)
K = np.flatnonzero(dgrid_valid[i,J])
J = J[K]
cc = fcmap[f2]
y = dgrid[i, J]
p1 = plt.plot(left + (k-k0)*np.ones_like(y), y,
fsmap[f2], color=cc, mec=cc, mfc='none',
zorder=20)
if not f2 in lp:
lp[f2] = p1[0]
xt.append(cnames[i])
if lastone:
#print 'xticks from', left, 'num', n, ':', xt
#plt.xticks(np.arange(len(xt)), xt, fontsize=8)
for ix,t in enumerate(xt):
plt.text(ix-0.05, 0, ' '+t, rotation=90, fontsize=10, va='bottom', ha='right',
color='k')
plt.xticks([])
plt.xlim(-0.5, len(xt)-0.5)
plt.ylabel('Density of matches')
plt.title(f1)
ymax = plt.axis()[3]
plt.ylim(-0.03 * ymax, ymax)
axes.append(plt.axis())
plt.axhline(0, color='k', alpha=0.5)
I = argsort_filters(lp.keys())
plt.figlegend([lp.values()[i] for i in I], [lp.keys()[i] for i in I], 'upper right')
#plt.figlegend(lp, uf, 'upper right')
# Expand axes to common range (vert only)
axes = np.array(axes)
ax0 = np.max(axes, axis=0)
ax1 = np.min(axes, axis=0)
ax = [ax0[0], ax1[1], ax0[2], ax1[3]]
for i,f1 in enumerate(uf):
plt.subplot(len(uf), 1, i+1)
#plt.axis(ax)
plt.ylim(ax[2],ax[3])
plt.suptitle('%s: matches by image' % name)
ps.savefig()