def checkDipoles(diaSrc):
dxdyp = []
dxdyn = []
for src in diaSrc:
centPosNaive = src.get("centroid.dipole.naive.pos")
centPosNaiveErr = src.get("centroid.dipole.naive.pos.err")
centPosNaiveFlag = src.get("centroid.dipole.naive.pos.flags")
fluxPosNaive = src.get("flux.dipole.naive.pos")
fluxPosNaiveErr = src.get("flux.dipole.naive.pos.err")
fluxPosNaiveFlag = src.get("flux.dipole.naive.pos.flags")
centNegNaive = src.get("centroid.dipole.naive.neg")
centNegNaiveErr = src.get("centroid.dipole.naive.neg.err")
centNegNaiveFlag = src.get("centroid.dipole.naive.neg.flags")
fluxNegNaive = src.get("flux.dipole.naive.neg")
fluxNegNaiveErr = src.get("flux.dipole.naive.neg.err")
fluxNegNaiveFlag = src.get("flux.dipole.naive.neg.flags")
######
centPosPsf = src.get("flux.dipole.psf.pos.centroid")
centPosPsfErr = src.get("flux.dipole.psf.pos.centroid.err")
centPosPsfFlag = src.get("flux.dipole.psf.pos.centroid.flags")
fluxPosPsf = src.get("flux.dipole.psf.pos")
fluxPosPsfErr = src.get("flux.dipole.psf.pos.err")
fluxPosPsfFlag = src.get("flux.dipole.psf.pos.flags")
centNegPsf = src.get("flux.dipole.psf.neg.centroid")
centNegPsfErr = src.get("flux.dipole.psf.neg.centroid.err")
centNegPsfFlag = src.get("flux.dipole.psf.neg.centroid.flags")
fluxNegPsf = src.get("flux.dipole.psf.neg")
fluxNegPsfErr = src.get("flux.dipole.psf.neg.err")
fluxNegPsfFlag = src.get("flux.dipole.psf.neg.flags")
chi2dof = src.get("flux.dipole.psf.chi2dof")
#print centPosPsf, centNegPsf, fluxPosPsf, fluxNegPsf, centPosPsfFlag, fluxPosPsfFlag, centNegPsfFlag, fluxNegPsfFlag
if not (centPosPsfFlag or fluxPosPsfFlag or centNegPsfFlag or fluxNegPsfFlag):
dx = centPosPsf[0] - centNegPsf[0]
dy = centPosPsf[1] - centNegPsf[1]
totalFlux = fluxPosPsf + fluxNegPsf
sigmaFlux = totalFlux / np.sqrt(fluxPosPsfErr**2 + fluxNegPsfErr**2)
if False:
print centNegPsf, centPosPsf, dx, dy, totalFlux, sigmaFlux, chi2dof
cmd = "regions command {line %f %f %f %f # color=magenta width=3}" % (centNegPsf[0], centNegPsf[1], centPosPsf[0], centPosPsf[1])
ds9.ds9Cmd(cmd, silent=False)
# Lets look at dy, since dx~0 for visits D and E
if dy < 0.0:
dxdyn.append((dx, dy))
else:
dxdyp.append((dx, dy))
dxn = np.array([x[0] for x in dxdyn])
dyn = np.array([x[1] for x in dxdyn])
dxp = np.array([x[0] for x in dxdyp])
dyp = np.array([x[1] for x in dxdyp])
return dxn,dyn,dxp,dyp
def ViewIntensityArrayInDs9(intensity_array, savefile=None):
import lsst.afw.display.ds9 as ds9
ds9.mtv(
makeImageFromArray(100 * intensity_array /
float(intensity_array.max())))
if savefile is not None:
arg = 'saveimage jpeg ' + str(savefile) + ' 100'
ds9.ds9Cmd(arg)
def imagePca(mosaics,
visits=None,
nComponent=3,
log=False,
rng=30,
showEigen=True,
showResiduals=False,
showOriginal=True,
showRecon=False,
normalizeEimages=True,
scale=False,
frame0=0,
verbose=False):
if showOriginal and showRecon:
raise RuntimeError(
"You may not specify both showOriginal and showRecon")
try:
rng[0]
except TypeError:
rng = [rng, rng]
if not visits:
visits = sorted(mosaics.keys())
mosaics = mosaics.copy()
for v in visits:
mosaics[v] = mosaics[v].clone()
pca = afwImage.ImagePcaF()
mask = None
for v in visits:
im = mosaics[v]
if not mask:
mask = im.Factory(im.getDimensions())
maska = mask.getArray()
X, Y = np.meshgrid(np.arange(mask.getWidth()),
np.arange(mask.getHeight()))
maska[np.where(np.hypot(X - 571, Y - 552) > 531)] = np.nan
del maska
mask[168:184, 701:838] = np.nan
mask[667:733, 420:556] = np.nan
mask[653:677, 548:570] = np.nan
mask[1031:1047, 274:414] = np.nan
if False:
mask[866:931, 697:828] = np.nan
mask[267:334, 145:276] = np.nan
im += mask
pca.addImage(im, afwMath.makeStatistics(im, afwMath.SUM).getValue())
pca.analyze()
eValues = np.array(pca.getEigenValues())
eValues /= eValues[0]
eImages = pca.getEigenImages()
#
# Fiddle eigen images (we don't care about orthogonality)
#
if False:
f10 = 0.1
f20 = -0.3
f30 = 0.55
eImages[1].getArray()[:] += f10 * eImages[0].getArray()
eImages[2].getArray()[:] += f20 * eImages[0].getArray()
eImages[3].getArray()[:] += f30 * eImages[0].getArray()
if nComponent:
eImages = eImages[:nComponent]
else:
nComponent = len(eImages)
#
# Normalize
#
good = np.where(np.isfinite(eImages[0].getArray()))
if normalizeEimages:
for eim in eImages:
eima = eim.getArray()
eima /= 1e-3 * np.sqrt(np.sum(eima[good]**2))
#
# Plot/display eigen values/images
#
frame = frame0
if showEigen:
for i in range(len(eImages)):
ds9.mtv(eImages[i], frame=frame, title="%d %.2g" % (i, eValues[i]))
ds9.ds9Cmd("scale linear; scale mode zscale")
frame += 1
pyplot.clf()
if log:
eValues = np.log10(eValues)
pyplot.plot(eValues)
pyplot.plot(eValues, marker='o')
pyplot.xlim(-0.5, len(eValues))
pyplot.ylim(max(-5, pyplot.ylim()[0]), 0.05 if log else 1.05)
pyplot.xlabel("n")
pyplot.ylabel(r"$lg(\lambda)$" if log else r"$\lambda$")
pyplot.show()
if showOriginal or showRecon or showResiduals:
#
# Expand input images in the (modified) eImages
#
A = np.empty(nComponent * nComponent).reshape((nComponent, nComponent))
b = np.empty(nComponent)
for v in visits:
im = mosaics[v]
if scale:
im /= afwMath.makeStatistics(im, afwMath.MEANCLIP).getValue()
for i in range(nComponent):
b[i] = np.dot(eImages[i].getArray()[good], im.getArray()[good])
for j in range(i, nComponent):
A[i, j] = np.dot(eImages[i].getArray()[good],
eImages[j].getArray()[good])
A[j, i] = A[i, j]
x = np.linalg.solve(A, b)
#print v, A, b, x
print "%d [%s] %s" % (v, ", ".join(["%9.2e" % _ for _ in x / x[0]
]), labels.get(v, ""))
recon = eImages[0].clone()
recon *= x[0]
recona = recon.getArray()
for i in range(1, nComponent):
recona += x[i] * eImages[i].getArray()
mtv = True
if showOriginal or showRecon:
if mtv:
ds9.mtv(im if showOriginal else recon,
frame=frame,
title=v)
else:
ds9.erase(frame=frame)
s0 = afwMath.makeStatistics(recon, afwMath.MEDIAN).getValue()
s0 -= 0.5 * rng[0]
ds9.ds9Cmd("scale linear; scale limits %g %g" %
(s0, s0 + rng[0]),
frame=frame)
ds9.dot("%s %d" % ("orig" if showOriginal else "resid", v),
int(0.5 * im.getWidth()),
int(0.15 * im.getHeight()),
frame=frame,
ctype=ds9.RED)
if labels.has_key(v):
ds9.dot(labels[v],
int(0.5 * im.getWidth()),
int(0.85 * im.getHeight()),
frame=frame,
ctype=ds9.RED)
frame += 1
if showResiduals:
recon -= im
if mtv:
ds9.mtv(recon, frame=frame)
else:
ds9.erase(frame=frame)
s0 = 0
s0 -= 0.5 * rng[1]
ds9.ds9Cmd("scale linear; scale limits %g %g" %
(s0, s0 + rng[1]),
frame=frame)
frame += 1
return eImages
def diffs(mosaics,
visits,
refVisit=None,
scale=True,
raw=None,
rng=20,
IRatioMax=1.0,
frame0=0,
verbose=False):
"""Display a series of differences and/or scaled differences
(scale = True, False, (True, False), ...)
"""
visits = list(visits) # in case it's an iterator, and to get a copy
if refVisit is None:
refVisit = visits[0]
ref = mosaics[refVisit]
nameRef = str(refVisit)
refMedian = float(np.median(ref.getArray()))
visits = [v for v in visits if v != refVisit]
try:
scale[0]
except TypeError:
scale = (scale, )
frame = frame0
goodVisits = [refVisit]
for v2 in visits:
for s in scale:
nameA, nameB = str(v2), nameRef
diff = mosaics[v2].clone()
IRatio = np.median(mosaics[v2].getArray()) / refMedian
if IRatioMax and IRatio > IRatioMax and IRatio < 1 / IRatioMax:
if verbose:
print "Skipping %d [ratio = %.2f]" % (v2, IRatio)
break
if s == scale[0]:
goodVisits.append(v2)
if s:
diff /= IRatio
nameA = "%s/%.2f" % (nameA, IRatio)
diffa = diff.getArray()
good = np.where(np.isfinite(diffa))
v2Median = np.mean(diffa[good])
if raw:
title = nameA
else:
diff -= ref
if IRatio < 1:
diff *= -1
nameA, nameB = nameB, nameA
if verbose:
print "%s %5.1f%% %-s" % (v2, 100 * np.mean(diffa[good]) /
v2Median, labels.get(v2, ""))
title = "%s - %s" % (nameA, nameB)
if not s:
title += " [rat=%.2f]" % (IRatio)
if True:
ds9.mtv(diff, title=title, frame=frame)
else:
ds9.erase(frame=frame)
ds9.dot(title,
int(0.5 * diff.getWidth()),
int(0.05 * diff.getHeight()),
frame=frame,
ctype=ds9.RED)
if labels.has_key(v2):
ds9.dot(labels[v2],
int(0.5 * diff.getWidth()),
int(0.95 * diff.getHeight()),
frame=frame,
ctype=ds9.RED)
s0 = 0 if (s and not raw) else afwMath.makeStatistics(
diff, afwMath.MEDIAN).getValue()
s0 -= 0.5 * rng
ds9.ds9Cmd("scale linear; scale limits %g %g" % (s0, s0 + rng),
frame=frame)
frame += 1
return list(sorted(goodVisits))
def imagePca(mosaics, visits=None, nComponent=3, log=False, rng=30,
showEigen=True, showResiduals=False, showOriginal=True, showRecon=False,
normalizeEimages=True, scale=False, frame0=0, verbose=False):
if showOriginal and showRecon:
raise RuntimeError("You may not specify both showOriginal and showRecon")
try:
rng[0]
except TypeError:
rng = [rng, rng]
if not visits:
visits = sorted(mosaics.keys())
mosaics = mosaics.copy()
for v in visits:
mosaics[v] = mosaics[v].clone()
pca = afwImage.ImagePcaF()
mask = None
for v in visits:
im = mosaics[v]
if not mask:
mask = im.Factory(im.getDimensions())
maska = mask.getArray()
X, Y = np.meshgrid(np.arange(mask.getWidth()), np.arange(mask.getHeight()))
maska[np.where(np.hypot(X - 571, Y - 552) > 531)] = np.nan
del maska
mask[ 168: 184, 701:838] = np.nan
mask[ 667: 733, 420:556] = np.nan
mask[ 653: 677, 548:570] = np.nan
mask[1031:1047, 274:414] = np.nan
if False:
mask[866:931, 697:828] = np.nan
mask[267:334, 145:276] = np.nan
im += mask
pca.addImage(im, afwMath.makeStatistics(im, afwMath.SUM).getValue())
pca.analyze()
eValues = np.array(pca.getEigenValues())
eValues /= eValues[0]
eImages = pca.getEigenImages()
#
# Fiddle eigen images (we don't care about orthogonality)
#
if False:
f10 = 0.1
f20 = -0.3
f30 = 0.55
eImages[1].getArray()[:] += f10*eImages[0].getArray()
eImages[2].getArray()[:] += f20*eImages[0].getArray()
eImages[3].getArray()[:] += f30*eImages[0].getArray()
if nComponent:
eImages = eImages[:nComponent]
else:
nComponent = len(eImages)
#
# Normalize
#
good = np.where(np.isfinite(eImages[0].getArray()))
if normalizeEimages:
for eim in eImages:
eima = eim.getArray()
eima /= 1e-3*np.sqrt(np.sum(eima[good]**2))
#
# Plot/display eigen values/images
#
frame = frame0
if showEigen:
for i in range(len(eImages)):
ds9.mtv(eImages[i], frame=frame, title="%d %.2g" % (i, eValues[i]))
ds9.ds9Cmd("scale linear; scale mode zscale")
frame += 1
pyplot.clf()
if log:
eValues = np.log10(eValues)
pyplot.plot(eValues)
pyplot.plot(eValues, marker='o')
pyplot.xlim(-0.5, len(eValues))
pyplot.ylim(max(-5, pyplot.ylim()[0]), 0.05 if log else 1.05)
pyplot.xlabel("n")
pyplot.ylabel(r"$lg(\lambda)$" if log else r"$\lambda$")
pyplot.show()
if showOriginal or showRecon or showResiduals:
#
# Expand input images in the (modified) eImages
#
A = np.empty(nComponent*nComponent).reshape((nComponent, nComponent))
b = np.empty(nComponent)
for v in visits:
im = mosaics[v]
if scale:
im /= afwMath.makeStatistics(im, afwMath.MEANCLIP).getValue()
for i in range(nComponent):
b[i] = np.dot(eImages[i].getArray()[good], im.getArray()[good])
for j in range(i, nComponent):
A[i, j] = np.dot(eImages[i].getArray()[good], eImages[j].getArray()[good])
A[j, i] = A[i, j]
x = np.linalg.solve(A, b)
#print v, A, b, x
print "%d [%s] %s" % (v, ", ".join(["%9.2e" % _ for _ in x/x[0]]), labels.get(v, ""))
recon = eImages[0].clone(); recon *= x[0]
recona = recon.getArray()
for i in range(1, nComponent):
recona += x[i]*eImages[i].getArray()
mtv = True
if showOriginal or showRecon:
if mtv:
ds9.mtv(im if showOriginal else recon, frame=frame, title=v)
else:
ds9.erase(frame=frame)
s0 = afwMath.makeStatistics(recon, afwMath.MEDIAN).getValue()
s0 -= 0.5*rng[0]
ds9.ds9Cmd("scale linear; scale limits %g %g" % (s0, s0 + rng[0]), frame=frame)
ds9.dot("%s %d" % ("orig" if showOriginal else "resid", v),
int(0.5*im.getWidth()), int(0.15*im.getHeight()), frame=frame, ctype=ds9.RED)
if labels.has_key(v):
ds9.dot(labels[v], int(0.5*im.getWidth()), int(0.85*im.getHeight()),
frame=frame, ctype=ds9.RED)
frame += 1
if showResiduals:
recon -= im
if mtv:
ds9.mtv(recon, frame=frame)
else:
ds9.erase(frame=frame)
s0 = 0
s0 -= 0.5*rng[1]
ds9.ds9Cmd("scale linear; scale limits %g %g" % (s0, s0 + rng[1]), frame=frame)
frame += 1
return eImages
def diffs(mosaics, visits, refVisit=None, scale=True, raw=None,
rng=20, IRatioMax=1.0, frame0=0, verbose=False):
"""Display a series of differences and/or scaled differences
(scale = True, False, (True, False), ...)
"""
visits = list(visits) # in case it's an iterator, and to get a copy
if refVisit is None:
refVisit = visits[0]
ref = mosaics[refVisit]
nameRef = str(refVisit)
refMedian = float(np.median(ref.getArray()))
visits = [v for v in visits if v != refVisit]
try:
scale[0]
except TypeError:
scale = (scale,)
frame = frame0
goodVisits = [refVisit]
for v2 in visits:
for s in scale:
nameA, nameB = str(v2), nameRef
diff = mosaics[v2].clone()
IRatio = np.median(mosaics[v2].getArray())/refMedian
if IRatioMax and IRatio > IRatioMax and IRatio < 1/IRatioMax:
if verbose:
print "Skipping %d [ratio = %.2f]" % (v2, IRatio)
break
if s == scale[0]:
goodVisits.append(v2)
if s:
diff /= IRatio
nameA = "%s/%.2f" % (nameA, IRatio)
diffa = diff.getArray()
good = np.where(np.isfinite(diffa))
v2Median = np.mean(diffa[good])
if raw:
title = nameA
else:
diff -= ref
if IRatio < 1:
diff *= -1
nameA, nameB = nameB, nameA
if verbose:
print "%s %5.1f%% %-s" % (v2, 100*np.mean(diffa[good])/v2Median, labels.get(v2, ""))
title = "%s - %s" % (nameA, nameB)
if not s:
title += " [rat=%.2f]" % (IRatio)
if True:
ds9.mtv(diff, title=title, frame=frame)
else:
ds9.erase(frame=frame)
ds9.dot(title, int(0.5*diff.getWidth()), int(0.05*diff.getHeight()),
frame=frame, ctype=ds9.RED)
if labels.has_key(v2):
ds9.dot(labels[v2], int(0.5*diff.getWidth()), int(0.95*diff.getHeight()),
frame=frame, ctype=ds9.RED)
s0 = 0 if (s and not raw) else afwMath.makeStatistics(diff, afwMath.MEDIAN).getValue()
s0 -= 0.5*rng
ds9.ds9Cmd("scale linear; scale limits %g %g" % (s0, s0 + rng), frame=frame)
frame += 1
return list(sorted(goodVisits))
showBadCandidates=showBadCandidates)
maUtils.showPsfCandidates(exposure, psfCellSet, psf=psf, frame=5,
normalize=normalizeResiduals,
showBadCandidates=showBadCandidates,
variance=True)
except:
if not showBadCandidates:
showBadCandidates = True
continue
break
if displayPsfComponents:
maUtils.showPsf(psf, eigenValues, frame=6)
if displayPsfMosaic:
maUtils.showPsfMosaic(exposure, psf, frame=7, showFwhm=True)
ds9.ds9Cmd(ds9.selectFrame(frame=7) + " ;scale limits 0 1")
if displayPsfSpatialModel:
maUtils.plotPsfSpatialModel(exposure, psf, psfCellSet, showBadCandidates=True,
matchKernelAmplitudes=matchKernelAmplitudes,
keepPlots=keepMatplotlibPlots)
if pause:
while True:
try:
reply = raw_input("Next iteration? [ynchpqQs] ").strip()
except EOFError:
reply = "n"
reply = reply.split()
if reply:
reply, args = reply[0], reply[1:]
import os
import numpy as np
import lsst.afw.display.ds9 as ds9
try:
ds9.initDS9(False)
except ds9.Ds9Error:
print 'DS9 launch bug error thrown away (probably)'
from my_functions import OpenTimepixInDS9
# OpenTimepixInDS9('/mnt/hgfs/VMShared/Data/new_sensors/oxford/march_2015/E404_50nm_newsample/420@133_1.txt')
# OpenTimepixInDS9('/mnt/hgfs/VMShared/Data/new_sensors/suny_01042015/50nm_big_runs/422_big_only_electrons/1@1_1.txt')
# OpenTimepixInDS9('/mnt/hgfs/VMShared/Data/new_sensors/suny_01042015/50nm_big_runs/422_big_only_ions/1@1_1.txt')
OpenTimepixInDS9('/mnt/hgfs/VMShared/Data/new_sensors/bnl/24_03_2015/A2(300nm)/Run4/1_0001.txt')
ds9.ds9Cmd('-scale limits 6400 6600')
ds9.ds9Cmd('-scale limits 6400 6600')
exit()
expids,durations = np.loadtxt('/mnt/hgfs/VMShared/temp/input.txt', delimiter = '\t', skiprows = 1, usecols = (0,1), unpack = True)
for i in range(len(expids)):
print i, str(expids[i]), str(durations[i])
dl = []
files = os.listdir('/mnt/hgfs/VMShared/Data/des_darks/')
for filename in files:
dl.append(filename[8:14])
footPrints = footPrintSet.getFootprints()
footprint_pixels = 0
ions_per_frame.append(len(footPrints))
for footprint in footPrints:
if DISPLAY and filenum == display_num: displayUtils.drawBBox(footprint.getBBox(), borderWidth=0.5) # border to fully encompass the bbox and no more
npix = afwDetect.Footprint.getNpix(footprint)
cluster_sizes.append(npix)
footprint_pixels += npix
footprint_pixels_per_frame_list.append(footprint_pixels)
# if filenum == display_num: exit()
if DISPLAY and filenum == display_num:
arg = 'saveimage jpeg ' + str(OUTPUT_PATH + str(val) + '_example_frame.jpeg') + ' 100'
ds9.ds9Cmd(arg)
histmax = 30
name = 'cluster_size'
h1 = ListToHist(cluster_sizes, OUTPUT_PATH + ID + '_cluster_size.png', log_z = False, nbins = histmax-1, histmin = 1, histmax = histmax, name = name)
histmax = 20001#201
name = 'pixels_per_frame'
h2 = ListToHist(pixels_per_frame_list, OUTPUT_PATH + ID + '_pixels_per_frame.png', log_z = False, nbins = 100, histmin = 1, histmax = histmax, name = name)
histmax = 20001#20001
name = 'footprint_pixels_per_frame'
h4 = ListToHist(footprint_pixels_per_frame_list, OUTPUT_PATH + ID + '_footprint_pixels_per_frame.png', log_z = False, nbins = 100, histmin = 1, histmax = histmax, name = name)
name = 'footprint_pixels_per_frame_big_bins'
h5 = ListToHist(footprint_pixels_per_frame_list, OUTPUT_PATH + ID + '_footprint_pixels_per_frame_big_bins.png', log_z = False, nbins = 50, histmin = 1, histmax = histmax, name = name)
mapper = Mapper(root=os.path.join(indir, "outputs8%s%s_doPreConvolveTrue" % (suffixT, suffixI)), calibRoot = None, outputRoot = None)
butler = dafPersist.ButlerFactory(mapper = mapper).create()
dataId = {"filter": filterName, "visit": visit, "raft": "2,2", "sensor": "1,1"}
calexp = butler.get(datasetType="calexp", dataId=dataId)
rotTel = calexp.getMetadata().get("SPIDANG")
zenith = calexp.getMetadata().get("ZENITH")
diffim = butler.get(datasetType="deepDiff_differenceExp", dataId=dataId)
src = butler.get(datasetType="src", dataId=dataId)
matches = astrometer.useKnownWcs(src, exposure=calexp).matches
if filterId == 1 and airmassIdI == 0:
# Make sure ds9 has all the frames initialized
for i in range(1, 10): ds9.mtv(calexp, frame=i)
ds9.mtv(calexp, frame=frameId)
ds9.ds9Cmd("mask transparency 80")
if suffixI != "C":
for match in matches:
refObj = match.first
gr = -2.5 * np.log10(refObj["g"]) - -2.5 * np.log10(refObj["r"])
amp = getDcrAmp(zenith, filterName, gr)
if amp == 0:
continue
if gr < 0: color="blue"
elif gr < 1: color="green"
else: color="red"
src = match.second
x1, y1 = src.getCentroid() # End of the Dcr vector
dx = amp * -np.sin(np.pi * -rotTel / 180.) # Minus since we are rotating clockwise from y-axis
dy = amp * np.cos(np.pi * -rotTel / 180.)
x0 = x1 - dx
frameId = 1
dxns = np.array(())
dyns = np.array(())
dxps = np.array(())
dyps = np.array(())
for sx in range(3):
for sy in range(3):
sensor = "%d,%d" % (sx, sy)
dataId = {"visit": visit, "raft": "2,2", "sensor": sensor}
if not butler.datasetExists(datasetType="deepDiff_differenceExp", dataId=dataId):
continue
src = butler.get(datasetType="deepDiff_diaSrc", dataId = dataId)
if False:
diffim = butler.get(datasetType="deepDiff_differenceExp", dataId=dataId)
ds9.mtv(diffim, frame=frameId); frameId += 1
ds9.ds9Cmd("mask transparency 80")
#print "#", suffixT, suffixI, dataId
dxn, dyn, dxp, dyp = checkDipoles(src)
dxns = np.append(dxns, dxn)
dyns = np.append(dyns, dyn)
dxps = np.append(dxps, dxp)
dyps = np.append(dyps, dyp)
distn = np.sqrt(dxns**2 + dyns**2) * 0.2
angn = np.arctan(dyns/dxns) * 180 / np.pi
distp = np.sqrt(dxps**2 + dyps**2) * 0.2
angp = np.arctan(dyps/dxps) * 180 / np.pi
# OK, so for dy < 0, this is being measured w.r.t. x axis; in our coordinate system take abs and add 90.
angn = 90 + np.abs(angn)
# And for dy > 0 its 270 + np.abs
showBadCandidates=showBadCandidates,
variance=True)
except:
if not showBadCandidates:
showBadCandidates = True
continue
break
if displayPsfComponents:
maUtils.showPsf(psf, eigenValues, frame=6)
if displayPsfMosaic:
maUtils.showPsfMosaic(exposure,
psf,
frame=7,
showFwhm=True)
ds9.ds9Cmd(ds9.selectFrame(frame=7) + " ;scale limits 0 1")
if displayPsfSpatialModel:
maUtils.plotPsfSpatialModel(
exposure,
psf,
psfCellSet,
showBadCandidates=True,
matchKernelAmplitudes=matchKernelAmplitudes,
keepPlots=keepMatplotlibPlots)
if pause:
while True:
try:
reply = raw_input(
"Next iteration? [ynchpqQs] ").strip()
except EOFError:
