def ngc1851_image():
"""
Plot an image of NGC 1851 for the paper.
"""
image_root = '/u/jlu/data/gsaoi/commission/reduce/ngc1851/combo/ngc1851'
# Load up the PSF stars file to get the coordinates.
stars = atpy.Table(image_root + '_psf_stars_pixel.txt', type='ascii')
stars.Xarc = stars.X * scale
stars.Yarc = stars.Y * scale
scale = 0.00995
# gc = aplpy.FITSFigure(image_file)
# gc.show_grayscale()
img = pyfits.getdata(image_root + '.fits')
img = img_scale.log(img, scale_min=0, scale_max=1e4)
#img = img_scale.sqrt(img, scale_min=500, scale_max=5e4)
# img = img_scale.linear(img, scale_min=500, scale_max=4e4)
xmin = ((0 - cooPix[0]) * scale * -1.0) + cooAsec[0]
xmax = ((img.shape[1] - cooPix[0]) * scale * -1.0) + cooAsec[0]
ymin = ((0 - cooPix[1]) * scale) + cooAsec[1]
ymax = ((img.shape[0] - cooPix[1]) * scale) + cooAsec[1]
extent = [xmin, xmax, ymin, ymax]
py.clf()
py.imshow(img, extent=extent, cmap=py.cm.gray_r)
def ngc1851_image():
"""
Plot an image of NGC 1851 for the paper.
"""
image_root = '/u/jlu/data/gsaoi/commission/reduce/ngc1851/combo/ngc1851'
# Load up the PSF stars file to get the coordinates.
stars = atpy.Table(image_root + '_psf_stars_pixel.txt', type='ascii')
stars.Xarc = stars.X * scale
stars.Yarc = stars.Y * scale
scale = 0.00995
# gc = aplpy.FITSFigure(image_file)
# gc.show_grayscale()
img = pyfits.getdata(image_root + '.fits')
img = img_scale.log(img, scale_min=0, scale_max=1e4)
#img = img_scale.sqrt(img, scale_min=500, scale_max=5e4)
# img = img_scale.linear(img, scale_min=500, scale_max=4e4)
xmin = ((0 - cooPix[0]) * scale * -1.0) + cooAsec[0]
xmax = ((img.shape[1] - cooPix[0]) * scale * -1.0) + cooAsec[0]
ymin = ((0 - cooPix[1]) * scale) + cooAsec[1]
ymax = ((img.shape[0] - cooPix[1]) * scale) + cooAsec[1]
extent = [xmin, xmax, ymin, ymax]
py.clf()
py.imshow(img, extent=extent, cmap=py.cm.gray_r)
def mosaic_kp():
"""
Make a mosaic of our NIRC2 data on W51, one per filter.
"""
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 = [6500, 6500, 6500, 5500]
scaleMaxL = [1600, 1300, 1400, 1600]
img = np.zeros((2400, 2400, 3), dtype=float)
imgH = np.zeros((2400, 2400), dtype=float)
imgK = np.zeros((2400, 2400), dtype=float)
imgL = np.zeros((2400, 2400), 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, hhdr = pyfits.getdata(rootH + '.fits', header=True)
k, khdr = pyfits.getdata(rootK + '.fits', header=True)
l, lhdr = pyfits.getdata(rootL + '.fits', header=True)
hint = hhdr['ITIME'] * hhdr['COADDS']
kint = khdr['ITIME'] * khdr['COADDS']
lint = lhdr['ITIME'] * lhdr['COADDS']
# 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.log(l[idx[0],idx[1]],
scale_min=scaleMinL[ii],
scale_max=scaleMaxL[ii])
img[idx[0],idx[1],1] = img_scale.log(k[idx[0], idx[1]],
scale_min=scaleMinK[ii],
scale_max=scaleMaxK[ii])
img[idx[0],idx[1],2] = img_scale.log(h[idx[0], idx[1]],
scale_min=scaleMinH[ii],
scale_max=scaleMaxH[ii])
imgH[idx[0], idx[1]] = h[idx[0], idx[1]] / hint
imgK[idx[0], idx[1]] = k[idx[0], idx[1]] / kint
imgL[idx[0], idx[1]] = l[idx[0], idx[1]] / lint
# Fix scaling of first image.
if ii == 0:
imgK[idx[0], idx[1]] -= 0.4
# Save on memory
l = None
k = None
h = None
l_new = None
k_new = None
h_new = None
# 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.figure(1)
# py.imshow(img[:,:,0], extent=extent, cmap=py.cm.gray)
# py.axis('equal')
py.figure(2)
py.imshow(img[:,:,1], extent=extent, cmap=py.cm.gray)
py.axis('equal')
# py.figure(3)
# py.imshow(img[:,:,2], extent=extent, cmap=py.cm.gray)
# py.axis('equal')
foo = raw_input('Continue?')
gcutil.rmall(['w51a_h_mosaic.fits',
'w51a_k_mosaic.fits',
'w51a_l_mosaic.fits'])
pyfits.writeto('w51a_h_mosaic.fits', imgH)
pyfits.writeto('w51a_k_mosaic.fits', imgK)
pyfits.writeto('w51a_l_mosaic.fits', imgL)
def mosaic_kp():
"""
Make a mosaic of our NIRC2 data on W51, one per filter.
"""
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 = [6500, 6500, 6500, 5500]
scaleMaxL = [1600, 1300, 1400, 1600]
img = np.zeros((2400, 2400, 3), dtype=float)
imgH = np.zeros((2400, 2400), dtype=float)
imgK = np.zeros((2400, 2400), dtype=float)
imgL = np.zeros((2400, 2400), 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, hhdr = pyfits.getdata(rootH + '.fits', header=True)
k, khdr = pyfits.getdata(rootK + '.fits', header=True)
l, lhdr = pyfits.getdata(rootL + '.fits', header=True)
hint = hhdr['ITIME'] * hhdr['COADDS']
kint = khdr['ITIME'] * khdr['COADDS']
lint = lhdr['ITIME'] * lhdr['COADDS']
# 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.log(l[idx[0], idx[1]],
scale_min=scaleMinL[ii],
scale_max=scaleMaxL[ii])
img[idx[0], idx[1], 1] = img_scale.log(k[idx[0], idx[1]],
scale_min=scaleMinK[ii],
scale_max=scaleMaxK[ii])
img[idx[0], idx[1], 2] = img_scale.log(h[idx[0], idx[1]],
scale_min=scaleMinH[ii],
scale_max=scaleMaxH[ii])
imgH[idx[0], idx[1]] = h[idx[0], idx[1]] / hint
imgK[idx[0], idx[1]] = k[idx[0], idx[1]] / kint
imgL[idx[0], idx[1]] = l[idx[0], idx[1]] / lint
# Fix scaling of first image.
if ii == 0:
imgK[idx[0], idx[1]] -= 0.4
# Save on memory
l = None
k = None
h = None
l_new = None
k_new = None
h_new = None
# 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.figure(1)
# py.imshow(img[:,:,0], extent=extent, cmap=py.cm.gray)
# py.axis('equal')
py.figure(2)
py.imshow(img[:, :, 1], extent=extent, cmap=py.cm.gray)
py.axis('equal')
# py.figure(3)
# py.imshow(img[:,:,2], extent=extent, cmap=py.cm.gray)
# py.axis('equal')
foo = raw_input('Continue?')
gcutil.rmall(
['w51a_h_mosaic.fits', 'w51a_k_mosaic.fits', 'w51a_l_mosaic.fits'])
pyfits.writeto('w51a_h_mosaic.fits', imgH)
pyfits.writeto('w51a_k_mosaic.fits', imgK)
pyfits.writeto('w51a_l_mosaic.fits', imgL)
