def make_color_ao_image():
h_img = pyfits.getdata(workdir + 'mag05jullgs_h_rot.fits')
h_imgScl = img_scale.sqrt(h_img, scale_min=50, scale_max=5500)
kp_img = pyfits.getdata(workdir + 'mag05jullgs_kp_rot.fits')
kp_imgScl = img_scale.sqrt(kp_img, scale_min=10, scale_max=17000)
lp_img = pyfits.getdata(workdir + 'mag05jullgs_lp_rot.fits')
lp_imgScl = img_scale.sqrt(lp_img, scale_min=-100, scale_max=60000)
sgra = np.array([540, 410])
scale = 0.00995
h_xextent = np.array([0, h_img.shape[0]])
h_yextent = np.array([0, h_img.shape[0]])
h_xextent = (h_xextent - sgra[0]) * -scale
h_yextent = (h_yextent - sgra[1]) * scale
h_extent = [h_xextent[0], h_xextent[-1], h_yextent[0], h_yextent[-1]]
img = np.zeros((h_img.shape[0], h_img.shape[1], 3), dtype=float)
img[:,:,0] = lp_imgScl
img[:,:,1] = kp_imgScl
img[:,:,2] = h_imgScl
py.figure(4, figsize=(10, 10))
py.clf()
py.subplots_adjust(left=0, right=1, bottom=0, top=1)
py.imshow(img, extent=h_extent)
ax = py.gca()
py.setp(ax.get_xticklabels(), visible=False)
py.setp(ax.get_yticklabels(), visible=False)
py.xlim(5, -5)
py.ylim(-4, 6)
py.savefig(workdir + 'img_lgsao_color.png')
def make_color_ao_image():
h_img = pyfits.getdata(workdir + 'mag05jullgs_h_rot.fits')
h_imgScl = img_scale.sqrt(h_img, scale_min=50, scale_max=5500)
kp_img = pyfits.getdata(workdir + 'mag05jullgs_kp_rot.fits')
kp_imgScl = img_scale.sqrt(kp_img, scale_min=10, scale_max=17000)
lp_img = pyfits.getdata(workdir + 'mag05jullgs_lp_rot.fits')
lp_imgScl = img_scale.sqrt(lp_img, scale_min=-100, scale_max=60000)
sgra = np.array([540, 410])
scale = 0.00995
h_xextent = np.array([0, h_img.shape[0]])
h_yextent = np.array([0, h_img.shape[0]])
h_xextent = (h_xextent - sgra[0]) * -scale
h_yextent = (h_yextent - sgra[1]) * scale
h_extent = [h_xextent[0], h_xextent[-1], h_yextent[0], h_yextent[-1]]
img = np.zeros((h_img.shape[0], h_img.shape[1], 3), dtype=float)
img[:, :, 0] = lp_imgScl
img[:, :, 1] = kp_imgScl
img[:, :, 2] = h_imgScl
py.figure(4, figsize=(10, 10))
py.clf()
py.subplots_adjust(left=0, right=1, bottom=0, top=1)
py.imshow(img, extent=h_extent)
ax = py.gca()
py.setp(ax.get_xticklabels(), visible=False)
py.setp(ax.get_yticklabels(), visible=False)
py.xlim(5, -5)
py.ylim(-4, 6)
py.savefig(workdir + 'img_lgsao_color.png')
def make_deep_ao_image():
img = pyfits.getdata(workdir + 'mag05jullgs_kp_rot.fits')
imgScl = img_scale.sqrt(img, scale_min=200, scale_max=15000)
sgra = np.array([538, 400])
scale = 0.00995
xextent = np.array([0, img.shape[0]])
yextent = np.array([0, img.shape[0]])
xextent = (xextent - sgra[0]) * -scale
yextent = (yextent - sgra[1]) * scale
extent = [xextent[0], xextent[-1], yextent[0], yextent[-1]]
py.figure(2, figsize=(6, 6))
py.clf()
py.subplots_adjust(left=0, right=1, bottom=0, top=1)
py.imshow(imgScl, cmap=py.cm.gray, extent=extent)
ax = py.gca()
py.setp(ax.get_xticklabels(), visible=False)
py.setp(ax.get_yticklabels(), visible=False)
py.xlim(3.7, -2.3)
py.ylim(-2.7, 3.3)
py.savefig(workdir + 'img_lgsao_deep.png')
def make_speckle_image():
speckle = pyfits.getdata(workdir + 'mag04jul_rot.fits')
speckImg = img_scale.sqrt(speckle, scale_min=0, scale_max=150)
sgra = np.array([483, 612])
scale = 0.0102
xextent = np.array([0, speckImg.shape[0]])
yextent = np.array([0, speckImg.shape[0]])
xextent = (xextent - sgra[0]) * -scale
yextent = (yextent - sgra[1]) * scale
extent = [xextent[0], xextent[-1], yextent[0], yextent[-1]]
py.figure(1, figsize=(6, 6))
py.clf()
py.subplots_adjust(left=0, right=1, bottom=0, top=1)
py.imshow(speckImg, cmap=py.cm.gray, extent=extent)
ax = py.gca()
py.setp(ax.get_xticklabels(), visible=False)
py.setp(ax.get_yticklabels(), visible=False)
py.axis('equal')
py.xlim(3.7, -2.3)
py.ylim(-2.8, 3.2)
py.savefig(workdir + 'img_speckle.png')
def make_deep_ao_image():
img = pyfits.getdata(workdir + 'mag05jullgs_kp_rot.fits')
imgScl = img_scale.sqrt(img, scale_min=200, scale_max=15000)
sgra = np.array([538, 400])
scale = 0.00995
xextent = np.array([0, img.shape[0]])
yextent = np.array([0, img.shape[0]])
xextent = (xextent - sgra[0]) * -scale
yextent = (yextent - sgra[1]) * scale
extent = [xextent[0], xextent[-1], yextent[0], yextent[-1]]
py.figure(2, figsize=(6, 6))
py.clf()
py.subplots_adjust(left=0, right=1, bottom=0, top=1)
py.imshow(imgScl, cmap=py.cm.gray, extent=extent)
ax = py.gca()
py.setp(ax.get_xticklabels(), visible=False)
py.setp(ax.get_yticklabels(), visible=False)
py.xlim(3.7, -2.3)
py.ylim(-2.7, 3.3)
py.savefig(workdir + 'img_lgsao_deep.png')
def make_speckle_image():
speckle = pyfits.getdata(workdir + 'mag04jul_rot.fits')
speckImg = img_scale.sqrt(speckle, scale_min=0, scale_max=150)
sgra = np.array([483, 612])
scale = 0.0102
xextent = np.array([0, speckImg.shape[0]])
yextent = np.array([0, speckImg.shape[0]])
xextent = (xextent - sgra[0]) * -scale
yextent = (yextent - sgra[1]) * scale
extent = [xextent[0], xextent[-1], yextent[0], yextent[-1]]
py.figure(1, figsize=(6, 6))
py.clf()
py.subplots_adjust(left=0, right=1, bottom=0, top=1)
py.imshow(speckImg, cmap=py.cm.gray, extent=extent)
ax = py.gca()
py.setp(ax.get_xticklabels(), visible=False)
py.setp(ax.get_yticklabels(), visible=False)
py.axis('equal')
py.xlim(3.7, -2.3)
py.ylim(-2.8, 3.2)
py.savefig(workdir + 'img_speckle.png')
def mosaic3color_ukidss():
"""
Make a 3 color mosaic of our NIRC2 data on W51.
"""
root = '/u/jlu/data/w51/ukidss/g48.9-0.3/ukidss_rgb'
j = pyfits.getdata(root + '_b_j.fits')
h = pyfits.getdata(root + '_g_h.fits')
k = pyfits.getdata(root + '_r_k.fits')
scale = [-0.2, 0.2]
img = np.zeros((j.shape[0], j.shape[1], 3), dtype=float)
img[:,:,0] = img_scale.sqrt(k, scale_min=0, scale_max=10000)
img[:,:,1] = img_scale.sqrt(h, scale_min=0, scale_max=10000)
img[:,:,2] = img_scale.sqrt(j, scale_min=0, scale_max=10000)
print img[:,:,0].min(), img[:,:,0].max()
print img[:,:,1].min(), img[:,:,1].max()
print img[:,:,2].min(), img[:,:,2].max()
origin = [j.shape[1]/2.0, j.shape[0]/2.0]
print origin
# Define the axes
xaxis = np.arange(-0.5, img.shape[1]+0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0]+0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
py.clf()
py.imshow(img, extent=extent)
py.axis('equal')
py.axis([60, -40, -30, 70])
py.xlabel('R.A. Offset (arcsec)')
py.ylabel('Dec. Offset (arcsec)')
py.title('UKIDSS JHK')
py.savefig('/u/jlu/work/w51/maps/w51a_3color_ukidss.png')
def mosaic3color_ukidss():
"""
Make a 3 color mosaic of our NIRC2 data on W51.
"""
root = '/u/jlu/data/w51/ukidss/g48.9-0.3/ukidss_rgb'
j = pyfits.getdata(root + '_b_j.fits')
h = pyfits.getdata(root + '_g_h.fits')
k = pyfits.getdata(root + '_r_k.fits')
scale = [-0.2, 0.2]
img = np.zeros((j.shape[0], j.shape[1], 3), dtype=float)
img[:, :, 0] = img_scale.sqrt(k, scale_min=0, scale_max=10000)
img[:, :, 1] = img_scale.sqrt(h, scale_min=0, scale_max=10000)
img[:, :, 2] = img_scale.sqrt(j, scale_min=0, scale_max=10000)
print img[:, :, 0].min(), img[:, :, 0].max()
print img[:, :, 1].min(), img[:, :, 1].max()
print img[:, :, 2].min(), img[:, :, 2].max()
origin = [j.shape[1] / 2.0, j.shape[0] / 2.0]
print origin
# Define the axes
xaxis = np.arange(-0.5, img.shape[1] + 0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0] + 0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
py.clf()
py.imshow(img, extent=extent)
py.axis('equal')
py.axis([60, -40, -30, 70])
py.xlabel('R.A. Offset (arcsec)')
py.ylabel('Dec. Offset (arcsec)')
py.title('UKIDSS JHK')
py.savefig('/u/jlu/work/w51/maps/w51a_3color_ukidss.png')
def coo_max(files, psfName, manual=True):
py.close(1)
py.figure(1, figsize=(10,10))
py.subplots_adjust(left=0.1, bottom=0.08, right=0.95, top=0.93)
firstFramePos = None
for _file in files:
fileDir, fileName = os.path.split(_file)
fileRoot, fileExt = os.path.splitext(fileName)
# Make a max file
_max = open(fileDir + fileRoot + '.max', 'w')
_max.write('35000\n')
_max.close()
# Read in the image for display to select a coo star
img, hdr = pyfits.getdata(_file, header=True)
imgRescale = img_scale.sqrt(img, scale_min=400, scale_max=5000)
fig = py.figure(1)
foo = fig.gca().get_xlabel()
if foo != '':
psfName = foo
print 'Getting from old plot:', psfName
py.clf()
py.imshow(imgRescale, aspect='equal', cmap=py.cm.gray)
py.title('{0} PA={1:.0f}'.format(fileRoot, hdr['PA']))
fig.gca().set_xlabel('{0}'.format(psfName))
print 'Plotting: ', psfName
py.ylabel('Press "p" for new star name.')
fig.canvas.mpl_connect('key_press_event', key_press)
starFound = False
while not starFound:
psfName = fig.gca().get_xlabel()
print 'In while: ', psfName
# Get the user selected PSF stars
if firstFramePos == None or manual == True:
pts = py.ginput(1, timeout=0)
xinit = int(round(pts[0][0]))
yinit = int(round(pts[0][1]))
else:
xinit = firstFramePos[0]
yinit = firstFramePos[1]
boxSize = 50
# Get an initial sub-image
imgSub, xLo, yLo = get_sub_image(img, boxSize, xinit, yinit)
# Find the maximum pixel value within this box...
# assume this is the star and re-center up on it
maxIdx = np.where(imgSub == imgSub.max())
xinit = xLo + maxIdx[1][0]
yinit = yLo + maxIdx[0][0]
# Get an sub-image centered on brightest pixel
imgSub, xLo, yLo = get_sub_image(img, boxSize, xinit, yinit)
yc, xc = scipy.ndimage.center_of_mass(imgSub)
xc += xLo
yc += yLo
print '%s Centroid: x=%.2f y=%.2f' % (fileRoot, xc, yc)
py.figure(2)
py.clf()
py.imshow(imgRescale, aspect='equal', cmap=py.cm.gray)
py.plot([xc], [yc], 'kx', ms=10)
py.xlim(xc - 30, xc + 30)
py.ylim(yc - 30, yc + 30)
py.figure(1)
gaussInfo = psf.moments(imgSub, SubSize=5)
g_height = gaussInfo[0]
g_muX = gaussInfo[1]
g_muY = gaussInfo[2]
g_FWHMX = gaussInfo[3]
g_FWHMY = gaussInfo[4]
g_FWHM = gaussInfo[5]
g_Ellip = gaussInfo[6]
g_angle = gaussInfo[7]
hdrout = '{0:5s} {1:5s} {2:5s} {3:5s} {4:5s} '
hdrout += '{5:5s} {6:4s} {7}\n'
strout = '{0:5.2f} {1:5.2f} {2:5.2f} {3:5.2f} {4:5.2f} '
strout += '{5:5.2f} {6:4.2f} {7:.1f}\n'
_gauss = open(fileDir + fileRoot + '.metrics', 'w')
_gauss.write(hdrout.format('muX', 'muY', 'FWHMX', 'FWHMY', 'FWHM',
'Ellip', 'Angle', 'Height'))
_gauss.write(strout.format(g_muX, g_muY, g_FWHMX, g_FWHMY, g_FWHM,
g_Ellip, g_angle, g_height))
_gauss.close()
# Make a max file
_coo = open(fileDir + fileRoot + '.coo', 'w')
_coo.write('{0:.2f} {1:.2f} {2}\n'.format(xc, yc, psfName))
_coo.close()
if firstFramePos == None:
firstFramePos = [xc, yc]
starFound = True
return
def mosaic3color():
"""
Make a 3 color mosaic of our NIRC2 data on W51.
"""
hepochs = ['09jun26', '09jun26', '09jun26', '09jun26']
kepochs = ['09jun10', '09jun10', '09jun10', '09jun26']
lepochs = ['09jun26', '09jun26', '09jun26', '09jun26']
cooStarsH = ['f1_psf0', 'f2_psf0', 'f3_psf0', 'f4_psf0']
cooStarsK = ['f1_psf0', 'f2_psf0', 'f3_psf0', 'f4_psf0']
cooStarsL = ['f1_psf1', 'f2_psf0', 'f3_psf2', 'f4_psf1']
cooStarsH = ['E4-1', 'E8-1', 'N5-1', 'W6-2']
cooStarsK = ['E4-1', 'E8-1', 'N5-1', 'W6-2']
cooStarsL = ['S0-1', 'E8-1', 'W7-1', 'W9-1']
scaleMinH = [0, 0, 0, 0]
scaleMinK = [0, 0, 0, 0]
scaleMinL = [1000, 1100, 1200, 1250]
scaleMaxH = [6000, 6000, 5000, 6000]
scaleMaxK = [5500, 5500, 5500, 4500]
# scaleMaxL = [1600, 1300, 1400, 1600]
scaleMaxL = [2000, 2000, 2000, 2000]
img = np.zeros((2400, 2400, 3), dtype=float)
origin = np.array([1200.0, 1200.0])
labelFile = '/u/jlu/data/w51/source_list/w51a_label.dat'
labels = starTables.Labels(labelFile=labelFile)
dataRoot = '/u/jlu/data/w51/'
py.clf()
foo = range(len(hepochs))
for ii in foo[::-1]:
# for ii in range(1):
rootH = '%s/%s/combo/mag%s_w51a_f%d_h' % \
(dataRoot, hepochs[ii], hepochs[ii], ii+1)
rootK = '%s/%s/combo/mag%s_w51a_f%d_kp' % \
(dataRoot, kepochs[ii], kepochs[ii], ii+1)
rootL = '%s/%s/combo/mag%s_w51a_f%d_lp' % \
(dataRoot, lepochs[ii], lepochs[ii], ii+1)
# Load up the images
h = pyfits.getdata(rootH + '.fits')
k = pyfits.getdata(rootK + '.fits')
l = pyfits.getdata(rootL + '.fits')
# Make the arrays into the largest size.
h_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
k_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
l_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
h_new[0:h.shape[0], 0:h.shape[1]] = h
k_new[0:k.shape[0], 0:k.shape[1]] = k
l_new[0:l.shape[0], 0:l.shape[1]] = l
# Load up the coo stars
tmpH = open(rootH + '.coo').readline().split()
cooH = np.array([float(tmpH[0]), float(tmpH[1])])
tmpK = open(rootK + '.coo').readline().split()
cooK = np.array([float(tmpK[0]), float(tmpK[1])])
tmpL = open(rootL + '.coo').readline().split()
cooL = np.array([float(tmpL[0]), float(tmpL[1])])
# Get the coordinates of each coo star in arcsec.
idxH = np.where(labels.name == cooStarsH[ii])[0][0]
idxK = np.where(labels.name == cooStarsK[ii])[0][0]
idxL = np.where(labels.name == cooStarsL[ii])[0][0]
asecH = np.array([labels.x[idxH], labels.y[idxH]])
asecK = np.array([labels.x[idxK], labels.y[idxK]])
asecL = np.array([labels.x[idxL], labels.y[idxL]])
scale = np.array([-0.00995, 0.00995])
# Now figure out the necessary shifts
originH = cooH - asecH/scale
originK = cooK - asecK/scale
originL = cooL - asecL/scale
# Shift the J and H images to be lined up with K-band
shiftL = origin - originL
shiftK = origin - originK
shiftH = origin - originH
l = interp.shift(l_new, shiftL[::-1])
k = interp.shift(k_new, shiftK[::-1])
h = interp.shift(h_new, shiftH[::-1])
print shiftH
print shiftL
xx, yy = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))
idx = np.where((h >= 1) & (k >= 1) & (l >= 1))
# Trim off the bottom 10 rows where there is data
ymin = yy[idx[0], idx[1]].min()
ydx = np.where(yy[idx[0],idx[1]] > (ymin+10))[0]
idx = (idx[0][ydx], idx[1][ydx])
img[idx[0],idx[1],0] = img_scale.sqrt(l[idx[0],idx[1]],
scale_min=scaleMinL[ii],
scale_max=scaleMaxL[ii])
img[idx[0],idx[1],1] = img_scale.sqrt(k[idx[0], idx[1]],
scale_min=scaleMinK[ii],
scale_max=scaleMaxK[ii])
img[idx[0],idx[1],2] = img_scale.sqrt(h[idx[0], idx[1]],
scale_min=scaleMinH[ii],
scale_max=scaleMaxH[ii])
# Defin the axes
xaxis = np.arange(-0.5, img.shape[1]+0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0]+0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
py.imshow(img, extent=extent)
py.axis('equal')
foo = raw_input('Continue?')
py.axis([7, -7, -7, 7])
py.savefig('/u/jlu/work/w51/maps/w51a_3color.png')
def arches_figure():
"""
Plot a 3 panel figure showing seeing-limited, HST, and AO data on the
Arches cluster to illustrate the power of AO.
"""
# ----------
# NIRC2
# ----------
hroot = 'mag06maylgs2_arch_f1_h'
kroot = 'mag06maylgs2_arch_f1_kp'
lroot = 'mag06maylgs2_arch_f1_lp'
cooStar = 'f1_psf0'
scaleMinH = 1000
scaleMinK = 800
scaleMinL = 600
scaleMaxH = 4500
scaleMaxK = 8000
scaleMaxL = 10000
img = np.zeros((1500, 1500, 3), dtype=float)
origin = np.array([750.0, 750.0])
labelFile = '/u/ghezgroup/data/gc/source_list/label_arch.dat'
labels = starTables.Labels(labelFile=labelFile)
dataDir = '/u/ghezgroup/data/gc/06maylgs2/combo/'
# Load up the images
h = pyfits.getdata(dataDir + hroot + '.fits')
k = pyfits.getdata(dataDir + kroot + '.fits')
l = pyfits.getdata(dataDir + lroot + '.fits')
# Make the arrays into the largest size.
h_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
k_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
l_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
h_new[0:h.shape[0], 0:h.shape[1]] = h
k_new[0:k.shape[0], 0:k.shape[1]] = k
l_new[0:l.shape[0], 0:l.shape[1]] = l
# Load up the coo stars
tmpH = open(dataDir + hroot + '.coo').readline().split()
cooH = np.array([float(tmpH[0]), float(tmpH[1])])
tmpK = open(dataDir + kroot + '.coo').readline().split()
cooK = np.array([float(tmpK[0]), float(tmpK[1])])
tmpL = open(dataDir + lroot + '.coo').readline().split()
cooL = np.array([float(tmpL[0]), float(tmpL[1])])
# Get the coordinates of each coo star in arcsec.
idxH = np.where(labels.name == cooStar)[0][0]
idxK = np.where(labels.name == cooStar)[0][0]
idxL = np.where(labels.name == cooStar)[0][0]
asecH = np.array([labels.x[idxH], labels.y[idxH]])
asecK = np.array([labels.x[idxK], labels.y[idxK]])
asecL = np.array([labels.x[idxL], labels.y[idxL]])
scale = np.array([-0.00995, 0.00995])
# Now figure out the necessary shifts
originH = cooH - asecH/scale
originK = cooK - asecK/scale
originL = cooL - asecL/scale
# Shift the J and H images to be lined up with K-band
shiftL = origin - originL
shiftK = origin - originK
shiftH = origin - originH
l = interp.shift(l_new, shiftL[::-1])
k = interp.shift(k_new, shiftK[::-1])
h = interp.shift(h_new, shiftH[::-1])
print shiftH
print shiftL
xx, yy = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))
idx = np.where((h >= 1) & (k >= 1) & (l >= 1))
# Trim off the bottom 10 rows where there is data
ymin = yy[idx[0], idx[1]].min()
ydx = np.where(yy[idx[0],idx[1]] > (ymin+10))[0]
idx = (idx[0][ydx], idx[1][ydx])
# gcutil.rmall(['arches_f1_h.fits', 'arches_f1_kp.fits', 'arches_f1_lp.fits'])
# pyfits.writeto('arches_f1_h.fits', h)
# pyfits.writeto('arches_f1_kp.fits', k)
# pyfits.writeto('arches_f1_lp.fits', l)
img[idx[0],idx[1],0] = img_scale.sqrt(l[idx[0],idx[1]],
scale_min=scaleMinL,
scale_max=scaleMaxL)
img[idx[0],idx[1],1] = img_scale.sqrt(k[idx[0], idx[1]],
scale_min=scaleMinK,
scale_max=scaleMaxK)
img[idx[0],idx[1],2] = img_scale.sqrt(h[idx[0], idx[1]],
scale_min=scaleMinH,
scale_max=scaleMaxH)
# Define the axes
xaxis = np.arange(-0.5, img.shape[1]+0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0]+0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
img_nirc2 = img
ext_nirc2 = extent
# ----------
# UKIDSS
# ----------
scaleMinJ = 20
scaleMinH = 400
scaleMinK = 1000
scaleMaxJ = 5000
scaleMaxH = 35000
scaleMaxK = 90000
dataDir = '/u/jlu/data/arches/ukidss/'
# Load up the images
j = pyfits.getdata(dataDir + 'ukidss_arches_j.fits')
h = pyfits.getdata(dataDir + 'ukidss_arches_h.fits')
k = pyfits.getdata(dataDir + 'ukidss_arches_k.fits')
img = np.zeros((j.shape[0], j.shape[1], 3), dtype=float)
origin = [173, 198]
scale = [-0.2, 0.2]
xx, yy = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))
img[:,:,0] = img_scale.sqrt(k,
scale_min=scaleMinK,
scale_max=scaleMaxK)
img[:,:,1] = img_scale.sqrt(h,
scale_min=scaleMinH,
scale_max=scaleMaxH)
img[:,:,2] = img_scale.sqrt(j,
scale_min=scaleMinJ,
scale_max=scaleMaxJ)
# Define the axes
xaxis = np.arange(-0.5, img.shape[1]+0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0]+0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
img_ukidss = img
ext_ukidss = extent
py.figure(2, figsize=(6,12))
py.clf()
py.subplots_adjust(bottom=0.05, top=0.95, hspace=0.25)
py.subplot(2, 1, 1)
py.imshow(img_ukidss, extent=ext_ukidss)
py.axis('equal')
py.axis([4.5, -6.5, -6.5, 4.5])
py.title('UKIDSS JHK')
py.xlabel('R.A. Offset (arcsec)')
py.ylabel('Dec. Offset (arcsec)')
py.subplot(2, 1, 2)
py.imshow(img_nirc2, extent=ext_nirc2)
py.axis('equal')
py.axis([4.5, -6.5, -6.5, 4.5])
py.title("Keck AO HK'L'")
py.xlabel('R.A. Offset (arcsec)')
py.ylabel('Dec. Offset (arcsec)')
py.savefig('arches_see_vs_ao.png')
def arches_figure():
"""
Plot a 3 panel figure showing seeing-limited, HST, and AO data on the
Arches cluster to illustrate the power of AO.
"""
# ----------
# NIRC2
# ----------
hroot = 'mag06maylgs2_arch_f1_h'
kroot = 'mag06maylgs2_arch_f1_kp'
lroot = 'mag06maylgs2_arch_f1_lp'
cooStar = 'f1_psf0'
scaleMinH = 1000
scaleMinK = 800
scaleMinL = 600
scaleMaxH = 4500
scaleMaxK = 8000
scaleMaxL = 10000
img = np.zeros((1500, 1500, 3), dtype=float)
origin = np.array([750.0, 750.0])
labelFile = '/u/ghezgroup/data/gc/source_list/label_arch.dat'
labels = starTables.Labels(labelFile=labelFile)
dataDir = '/u/ghezgroup/data/gc/06maylgs2/combo/'
# Load up the images
h = pyfits.getdata(dataDir + hroot + '.fits')
k = pyfits.getdata(dataDir + kroot + '.fits')
l = pyfits.getdata(dataDir + lroot + '.fits')
# Make the arrays into the largest size.
h_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
k_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
l_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
h_new[0:h.shape[0], 0:h.shape[1]] = h
k_new[0:k.shape[0], 0:k.shape[1]] = k
l_new[0:l.shape[0], 0:l.shape[1]] = l
# Load up the coo stars
tmpH = open(dataDir + hroot + '.coo').readline().split()
cooH = np.array([float(tmpH[0]), float(tmpH[1])])
tmpK = open(dataDir + kroot + '.coo').readline().split()
cooK = np.array([float(tmpK[0]), float(tmpK[1])])
tmpL = open(dataDir + lroot + '.coo').readline().split()
cooL = np.array([float(tmpL[0]), float(tmpL[1])])
# Get the coordinates of each coo star in arcsec.
idxH = np.where(labels.name == cooStar)[0][0]
idxK = np.where(labels.name == cooStar)[0][0]
idxL = np.where(labels.name == cooStar)[0][0]
asecH = np.array([labels.x[idxH], labels.y[idxH]])
asecK = np.array([labels.x[idxK], labels.y[idxK]])
asecL = np.array([labels.x[idxL], labels.y[idxL]])
scale = np.array([-0.00995, 0.00995])
# Now figure out the necessary shifts
originH = cooH - asecH / scale
originK = cooK - asecK / scale
originL = cooL - asecL / scale
# Shift the J and H images to be lined up with K-band
shiftL = origin - originL
shiftK = origin - originK
shiftH = origin - originH
l = interp.shift(l_new, shiftL[::-1])
k = interp.shift(k_new, shiftK[::-1])
h = interp.shift(h_new, shiftH[::-1])
print shiftH
print shiftL
xx, yy = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))
idx = np.where((h >= 1) & (k >= 1) & (l >= 1))
# Trim off the bottom 10 rows where there is data
ymin = yy[idx[0], idx[1]].min()
ydx = np.where(yy[idx[0], idx[1]] > (ymin + 10))[0]
idx = (idx[0][ydx], idx[1][ydx])
# gcutil.rmall(['arches_f1_h.fits', 'arches_f1_kp.fits', 'arches_f1_lp.fits'])
# pyfits.writeto('arches_f1_h.fits', h)
# pyfits.writeto('arches_f1_kp.fits', k)
# pyfits.writeto('arches_f1_lp.fits', l)
img[idx[0], idx[1], 0] = img_scale.sqrt(l[idx[0], idx[1]],
scale_min=scaleMinL,
scale_max=scaleMaxL)
img[idx[0], idx[1], 1] = img_scale.sqrt(k[idx[0], idx[1]],
scale_min=scaleMinK,
scale_max=scaleMaxK)
img[idx[0], idx[1], 2] = img_scale.sqrt(h[idx[0], idx[1]],
scale_min=scaleMinH,
scale_max=scaleMaxH)
# Define the axes
xaxis = np.arange(-0.5, img.shape[1] + 0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0] + 0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
img_nirc2 = img
ext_nirc2 = extent
# ----------
# UKIDSS
# ----------
scaleMinJ = 20
scaleMinH = 400
scaleMinK = 1000
scaleMaxJ = 5000
scaleMaxH = 35000
scaleMaxK = 90000
dataDir = '/u/jlu/data/arches/ukidss/'
# Load up the images
j = pyfits.getdata(dataDir + 'ukidss_arches_j.fits')
h = pyfits.getdata(dataDir + 'ukidss_arches_h.fits')
k = pyfits.getdata(dataDir + 'ukidss_arches_k.fits')
img = np.zeros((j.shape[0], j.shape[1], 3), dtype=float)
origin = [173, 198]
scale = [-0.2, 0.2]
xx, yy = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))
img[:, :, 0] = img_scale.sqrt(k, scale_min=scaleMinK, scale_max=scaleMaxK)
img[:, :, 1] = img_scale.sqrt(h, scale_min=scaleMinH, scale_max=scaleMaxH)
img[:, :, 2] = img_scale.sqrt(j, scale_min=scaleMinJ, scale_max=scaleMaxJ)
# Define the axes
xaxis = np.arange(-0.5, img.shape[1] + 0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0] + 0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
img_ukidss = img
ext_ukidss = extent
py.figure(2, figsize=(6, 12))
py.clf()
py.subplots_adjust(bottom=0.05, top=0.95, hspace=0.25)
py.subplot(2, 1, 1)
py.imshow(img_ukidss, extent=ext_ukidss)
py.axis('equal')
py.axis([4.5, -6.5, -6.5, 4.5])
py.title('UKIDSS JHK')
py.xlabel('R.A. Offset (arcsec)')
py.ylabel('Dec. Offset (arcsec)')
py.subplot(2, 1, 2)
py.imshow(img_nirc2, extent=ext_nirc2)
py.axis('equal')
py.axis([4.5, -6.5, -6.5, 4.5])
py.title("Keck AO HK'L'")
py.xlabel('R.A. Offset (arcsec)')
py.ylabel('Dec. Offset (arcsec)')
py.savefig('arches_see_vs_ao.png')
def mosaic3color():
"""
Make a 3 color mosaic of our NIRC2 data on W51.
"""
hepochs = ['09jun26', '09jun26', '09jun26', '09jun26']
kepochs = ['09jun10', '09jun10', '09jun10', '09jun26']
lepochs = ['09jun26', '09jun26', '09jun26', '09jun26']
cooStarsH = ['f1_psf0', 'f2_psf0', 'f3_psf0', 'f4_psf0']
cooStarsK = ['f1_psf0', 'f2_psf0', 'f3_psf0', 'f4_psf0']
cooStarsL = ['f1_psf1', 'f2_psf0', 'f3_psf2', 'f4_psf1']
cooStarsH = ['E4-1', 'E8-1', 'N5-1', 'W6-2']
cooStarsK = ['E4-1', 'E8-1', 'N5-1', 'W6-2']
cooStarsL = ['S0-1', 'E8-1', 'W7-1', 'W9-1']
scaleMinH = [0, 0, 0, 0]
scaleMinK = [0, 0, 0, 0]
scaleMinL = [1000, 1100, 1200, 1250]
scaleMaxH = [6000, 6000, 5000, 6000]
scaleMaxK = [5500, 5500, 5500, 4500]
# scaleMaxL = [1600, 1300, 1400, 1600]
scaleMaxL = [2000, 2000, 2000, 2000]
img = np.zeros((2400, 2400, 3), dtype=float)
origin = np.array([1200.0, 1200.0])
labelFile = '/u/jlu/data/w51/source_list/w51a_label.dat'
labels = starTables.Labels(labelFile=labelFile)
dataRoot = '/u/jlu/data/w51/'
py.clf()
foo = range(len(hepochs))
for ii in foo[::-1]:
# for ii in range(1):
rootH = '%s/%s/combo/mag%s_w51a_f%d_h' % \
(dataRoot, hepochs[ii], hepochs[ii], ii+1)
rootK = '%s/%s/combo/mag%s_w51a_f%d_kp' % \
(dataRoot, kepochs[ii], kepochs[ii], ii+1)
rootL = '%s/%s/combo/mag%s_w51a_f%d_lp' % \
(dataRoot, lepochs[ii], lepochs[ii], ii+1)
# Load up the images
h = pyfits.getdata(rootH + '.fits')
k = pyfits.getdata(rootK + '.fits')
l = pyfits.getdata(rootL + '.fits')
# Make the arrays into the largest size.
h_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
k_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
l_new = np.zeros((img.shape[0], img.shape[1]), dtype=float)
h_new[0:h.shape[0], 0:h.shape[1]] = h
k_new[0:k.shape[0], 0:k.shape[1]] = k
l_new[0:l.shape[0], 0:l.shape[1]] = l
# Load up the coo stars
tmpH = open(rootH + '.coo').readline().split()
cooH = np.array([float(tmpH[0]), float(tmpH[1])])
tmpK = open(rootK + '.coo').readline().split()
cooK = np.array([float(tmpK[0]), float(tmpK[1])])
tmpL = open(rootL + '.coo').readline().split()
cooL = np.array([float(tmpL[0]), float(tmpL[1])])
# Get the coordinates of each coo star in arcsec.
idxH = np.where(labels.name == cooStarsH[ii])[0][0]
idxK = np.where(labels.name == cooStarsK[ii])[0][0]
idxL = np.where(labels.name == cooStarsL[ii])[0][0]
asecH = np.array([labels.x[idxH], labels.y[idxH]])
asecK = np.array([labels.x[idxK], labels.y[idxK]])
asecL = np.array([labels.x[idxL], labels.y[idxL]])
scale = np.array([-0.00995, 0.00995])
# Now figure out the necessary shifts
originH = cooH - asecH / scale
originK = cooK - asecK / scale
originL = cooL - asecL / scale
# Shift the J and H images to be lined up with K-band
shiftL = origin - originL
shiftK = origin - originK
shiftH = origin - originH
l = interp.shift(l_new, shiftL[::-1])
k = interp.shift(k_new, shiftK[::-1])
h = interp.shift(h_new, shiftH[::-1])
print shiftH
print shiftL
xx, yy = np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))
idx = np.where((h >= 1) & (k >= 1) & (l >= 1))
# Trim off the bottom 10 rows where there is data
ymin = yy[idx[0], idx[1]].min()
ydx = np.where(yy[idx[0], idx[1]] > (ymin + 10))[0]
idx = (idx[0][ydx], idx[1][ydx])
img[idx[0], idx[1], 0] = img_scale.sqrt(l[idx[0], idx[1]],
scale_min=scaleMinL[ii],
scale_max=scaleMaxL[ii])
img[idx[0], idx[1], 1] = img_scale.sqrt(k[idx[0], idx[1]],
scale_min=scaleMinK[ii],
scale_max=scaleMaxK[ii])
img[idx[0], idx[1], 2] = img_scale.sqrt(h[idx[0], idx[1]],
scale_min=scaleMinH[ii],
scale_max=scaleMaxH[ii])
# Defin the axes
xaxis = np.arange(-0.5, img.shape[1] + 0.5, 1)
xaxis = ((xaxis - origin[0]) * scale[0])
yaxis = np.arange(-0.5, img.shape[0] + 0.5, 1)
yaxis = ((yaxis - origin[1]) * scale[1])
extent = [xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]]
py.imshow(img, extent=extent)
py.axis('equal')
foo = raw_input('Continue?')
py.axis([7, -7, -7, 7])
py.savefig('/u/jlu/work/w51/maps/w51a_3color.png')
def coo_max(files, psfName, manual=True):
py.close(1)
py.figure(1, figsize=(10, 10))
py.subplots_adjust(left=0.1, bottom=0.08, right=0.95, top=0.93)
firstFramePos = None
for _file in files:
fileDir, fileName = os.path.split(_file)
fileRoot, fileExt = os.path.splitext(fileName)
# Make a max file
_max = open(fileDir + fileRoot + '.max', 'w')
_max.write('35000\n')
_max.close()
# Read in the image for display to select a coo star
img, hdr = pyfits.getdata(_file, header=True)
imgRescale = img_scale.sqrt(img, scale_min=400, scale_max=5000)
fig = py.figure(1)
foo = fig.gca().get_xlabel()
if foo != '':
psfName = foo
print 'Getting from old plot:', psfName
py.clf()
py.imshow(imgRescale, aspect='equal', cmap=py.cm.gray)
py.title('{0} PA={1:.0f}'.format(fileRoot, hdr['PA']))
fig.gca().set_xlabel('{0}'.format(psfName))
print 'Plotting: ', psfName
py.ylabel('Press "p" for new star name.')
fig.canvas.mpl_connect('key_press_event', key_press)
starFound = False
while not starFound:
psfName = fig.gca().get_xlabel()
print 'In while: ', psfName
# Get the user selected PSF stars
if firstFramePos == None or manual == True:
pts = py.ginput(1, timeout=0)
xinit = int(round(pts[0][0]))
yinit = int(round(pts[0][1]))
else:
xinit = firstFramePos[0]
yinit = firstFramePos[1]
boxSize = 50
# Get an initial sub-image
imgSub, xLo, yLo = get_sub_image(img, boxSize, xinit, yinit)
# Find the maximum pixel value within this box...
# assume this is the star and re-center up on it
maxIdx = np.where(imgSub == imgSub.max())
xinit = xLo + maxIdx[1][0]
yinit = yLo + maxIdx[0][0]
# Get an sub-image centered on brightest pixel
imgSub, xLo, yLo = get_sub_image(img, boxSize, xinit, yinit)
yc, xc = scipy.ndimage.center_of_mass(imgSub)
xc += xLo
yc += yLo
print '%s Centroid: x=%.2f y=%.2f' % (fileRoot, xc, yc)
py.figure(2)
py.clf()
py.imshow(imgRescale, aspect='equal', cmap=py.cm.gray)
py.plot([xc], [yc], 'kx', ms=10)
py.xlim(xc - 30, xc + 30)
py.ylim(yc - 30, yc + 30)
py.figure(1)
gaussInfo = psf.moments(imgSub, SubSize=5)
g_height = gaussInfo[0]
g_muX = gaussInfo[1]
g_muY = gaussInfo[2]
g_FWHMX = gaussInfo[3]
g_FWHMY = gaussInfo[4]
g_FWHM = gaussInfo[5]
g_Ellip = gaussInfo[6]
g_angle = gaussInfo[7]
hdrout = '{0:5s} {1:5s} {2:5s} {3:5s} {4:5s} '
hdrout += '{5:5s} {6:4s} {7}\n'
strout = '{0:5.2f} {1:5.2f} {2:5.2f} {3:5.2f} {4:5.2f} '
strout += '{5:5.2f} {6:4.2f} {7:.1f}\n'
_gauss = open(fileDir + fileRoot + '.metrics', 'w')
_gauss.write(
hdrout.format('muX', 'muY', 'FWHMX', 'FWHMY', 'FWHM', 'Ellip',
'Angle', 'Height'))
_gauss.write(
strout.format(g_muX, g_muY, g_FWHMX, g_FWHMY, g_FWHM, g_Ellip,
g_angle, g_height))
_gauss.close()
# Make a max file
_coo = open(fileDir + fileRoot + '.coo', 'w')
_coo.write('{0:.2f} {1:.2f} {2}\n'.format(xc, yc, psfName))
_coo.close()
if firstFramePos == None:
firstFramePos = [xc, yc]
starFound = True
return
