fef = pyfits.open('big_mosaic_feii.fits')
fe = fef[0].data
if os.path.exists('big_mosaic_feii_unsharp.fits'):
feunsharp = pyfits.getdata('big_mosaic_feii_unsharp.fits')
fenormed = pyfits.getdata('big_mosaic_feii_normed.fits')
else:
#kernel = astropy.nddata.convolution.make_kernel.make_kernel([151,151],kernelwidth=50)
#fesmooth = astropy.nddata.convolve(fe,kernel)
mimg = mask_image(fe, downsample=4)
fesmooth = agpy.smooth(mimg,
250,
fft_pad=False,
interpolate_nan=True,
psf_pad=False,
ignore_edge_zeros=True,
normalize_kernel=True,
use_numpy_fft=True,
nthreads=1,
use_rfft=True,
complextype=np.float32,
silent=False,
boundary='fill')
feunsharp = fe
for ii in xrange(4):
for jj in xrange(4):
shape = feunsharp[ii::4, jj::4].shape
feunsharp[ii::4, jj::4] -= fesmooth[:shape[0], :shape[1]]
fef[0].data = feunsharp
fef.writeto("big_mosaic_feii_unsharp.fits", clobber=True)
fenormed = fe
for ii in xrange(4):
#FeII_NS_mosaic.fits H2_NS_mosaic.fits H2_mosaic.fits KS_mosaic.fits Ks_NS_mosaic.fits
fef = pyfits.open('Fe_NS_mosaic_bgm.fits')
fe = fef[0].data
if os.path.exists('Fe_NS_mosaic_bgm_unsharp.fits'):
feunsharp = pyfits.getdata('Fe_NS_mosaic_bgm_unsharp.fits')
fenormed = pyfits.getdata('Fe_NS_mosaic_bgm_normed.fits')
else:
#kernel = astropy.nddata.convolution.make_kernel.make_kernel([151,151],kernelwidth=50)
#fesmooth = astropy.nddata.convolve(fe,kernel)
fesmooth = agpy.smooth(fe[::4, ::4],
250,
fft_pad=False,
interpolate_nan=True,
psf_pad=False,
ignore_edge_zeros=True,
normalize_kernel=True,
use_numpy_fft=True,
nthreads=1,
use_rfft=True,
complextype=np.float32,
silent=False,
boundary='wrap')
feunsharp = fe
for ii in xrange(4):
for jj in xrange(4):
feunsharp[ii::4, jj::4] -= fesmooth
fef[0].data = feunsharp
fef.writeto("Fe_NS_mosaic_bgm_unsharp.fits", clobber=True)
fenormed = fe
for ii in xrange(4):
for jj in xrange(4):
if not 'fe' in locals():
import pyfits
#FeII_NS_mosaic.fits H2_NS_mosaic.fits H2_mosaic.fits KS_mosaic.fits Ks_NS_mosaic.fits
# fe_montage_georgs_southeast.fits
# h2_montage_georgs_southeast.fits
# ks_montage_georgs_southeast.fits
fef = pyfits.open('fe_montage_georgs_southeast.fits')
fe = fef[0].data
if os.path.exists('fe_montage_georgs_southeast_unsharp.fits'):
feunsharp = pyfits.getdata('fe_montage_georgs_southeast_unsharp.fits')
fenormed = pyfits.getdata('fe_montage_georgs_southeast_normed.fits')
else:
#kernel = astropy.nddata.convolution.make_kernel.make_kernel([151,151],kernelwidth=50)
#fesmooth = astropy.nddata.convolve(fe,kernel)
fesmooth = agpy.smooth(fe[::4,::4],250, fft_pad=False, interpolate_nan=True, psf_pad=False,
ignore_edge_zeros=True, normalize_kernel=True, use_numpy_fft=True, nthreads=1,
use_rfft=True, complextype=np.float32, silent=False, boundary='wrap')
feunsharp = fe
for ii in xrange(4):
for jj in xrange(4):
feunsharp[ii::4,jj::4] -= fesmooth
fef[0].data = feunsharp
fef.writeto("fe_montage_georgs_southeast_unsharp.fits",clobber=True)
fenormed = fe
for ii in xrange(4):
for jj in xrange(4):
fenormed[ii::4,jj::4] /= fesmooth
fef[0].data = fenormed
fef.writeto("fe_montage_georgs_southeast_normed.fits",clobber=True)
# too big fesmooth = astropy.smooth(fe,100,ignore_nan=True)
#b2 = pyfits.open('GEMS_B2_Trapezium_mosaic_bgmatch.fits')
ylabel('Power (Jy$^2$)')
axis([0.00015, 0.15, 5e2, 1e10])
savefig('deconv_sim_wavenum_PSDSs.png')
if 1:
for ii, rad in enumerate([
100, 210, 240, 270, 300, 330, 360, 400, 500, 600, 700, 800, 900,
1000
]):
if (ii % nprocs != myrank):
continue
#brfs[rad] = exp(-(rr*7.2)**2/(2.0*(rad/2.35)**2)) / exp(-(rr*7.2)**2/(2.0*(rad/2.35)**2)).sum()
#kernel = numpy.sinc(2*numpy.pi*rr/rad)
#kernel /= kernel.sum()
#brsms[rad] = convolve(imgf,kernel)
brickwalled = smooth(imgf, rad / 7.2, 'brickwall')
gaussianed = smooth(imgf, rad / 2.35 / 7.2, 'gaussian')
tophatted = smooth(imgf, rad / 7.2, 'tophat')
figure(ii)
clf()
spectral()
subplot(221)
imshow(img, vmin=-1, vmax=10)
title("Input Image")
colorbar()
subplot(222)
imshow((img - pipeimg) - tophatted[:1155, :1151], vmin=-1, vmax=1)
title('Filtered with %i" radius tophat' % rad)
colorbar()
subplot(223)
imshow((img - pipeimg) - gaussianed[:1155, :1151], vmin=-1, vmax=1)
sourceimg/=sourceimg.sum() print "Filling Factor: ",(beamimg*sourceimg).sum() pl.figure(11) pl.clf() pl.subplot(1,3,1) pl.imshow(beamimg) pl.colorbar() pl.subplot(1,3,2) pl.imshow(sourceimg) pl.colorbar() pl.subplot(1,3,3) pl.imshow(beamimg*sourceimg) pl.colorbar() sourcepars = [0, 1, xc-30/15., yc+30/15., width_x_as.value, width_y_as.value, rota] sourceimgshift = gaussfitter.twodgaussian(sourcepars, shape=shape) sourceimgshift/=sourceimgshift.sum() print "Filling Factor (offset by 30,30): ",(beamimg*sourceimgshift).sum() sourcepars = [0, 1, xc-90/15., yc+90/15., width_x_as.value, width_y_as.value, rota] sourceimgshift = gaussfitter.twodgaussian(sourcepars, shape=shape) sourceimgshift/=sourceimgshift.sum() print "Filling Factor (offset by 90,90): ",(beamimg*sourceimgshift).sum() print "The filling factor appears to not be the correction factor for the elliptical gaussian." from agpy import smooth # to simulate what is done to the 2 cm image... si = sourceimg.copy() si /= si.max() print "The correction factor is ",smooth(si,4).max()
import pyfits
#fe_NS_mosaic.fits H2_NS_mosaic.fits H2_mosaic.fits KS_mosaic.fits Ks_NS_mosaic.fits
# fe_montage_georgs_southeast.fits
# altair_big_h2.fits
# fe_montage_georgs_southeast.fits
fef = pyfits.open('altair_big_fe.fits')
fe = fef[0].data
if os.path.exists('altair_big_fe_unsharp.fits'):
feunsharp = pyfits.getdata('altair_big_fe_unsharp.fits')
fenormed = pyfits.getdata('altair_big_fe_normed.fits')
else:
#kernel = astropy.nddata.convolution.make_kernel.make_kernel([151,151],kernelwidth=50)
#fesmooth = astropy.nddata.convolve(fe,kernel)
fesmooth = agpy.smooth(mask_image(fe,downsample=4),250, fft_pad=False,
interpolate_nan=True, psf_pad=False, ignore_edge_zeros=True,
normalize_kernel=True, use_numpy_fft=True, nthreads=1,
use_rfft=True, complextype=np.float32, silent=False,
boundary='fill')
feunsharp = fe
for ii in xrange(4):
for jj in xrange(4):
shape = feunsharp[ii::4,jj::4].shape
feunsharp[ii::4,jj::4] -= fesmooth[:shape[0],:shape[1]]
fef[0].data = feunsharp
fef.writeto("altair_big_fe_unsharp.fits",clobber=True)
fenormed = fe
for ii in xrange(4):
for jj in xrange(4):
shape = fenormed[ii::4,jj::4].shape
fenormed[ii::4,jj::4] /= fesmooth[:shape[0],:shape[1]]
fef[0].data = fenormed
cont_offset = (2.73*u.K).to(u.Jy,u.brightness_temperature(beam,14.488*u.GHz))
extra_cont_offset = 0.05
contlev = cont_offset.value + extra_cont_offset + noisefloor
#spec2 = f2[0].data[0,350:600,:,:] + 0.03
spec2 = f2[0].data[190:293,:,:] + contlev + cont2
# try to "subtract out" the negative continuum...
#spec2[:,cont2<0] -= cont2[cont2<0]
dosmooth = False
if dosmooth:
spec2s = cube_regrid.spatial_smooth_cube(spec2,kernelwidth=1.5)
cont2s = agpy.smooth(cont2+contlev,kernelwidth=1.5)
else:
spec2s = spec2
cont2s = cont2
spec2s[:,cont2s<0] -= cont2s[cont2s<0]
#cont2s[cont2s<contlev]=contlev
#cont2 += contlev
#cont2[cont2<contlev+noisefloor]=contlev+noisefloor
# "noise floor" (not really)
#spec2[spec2<0.01] = 0.01
#tau2 = -np.log((spec2/cont2))
tau2 = -np.log((spec2s/cont2s))
tau2[np.isnan(tau2)] = 5
cube22.units = "K"
cube44 = pyspeckit.Cube('hotclump_44.cube_r0.5_contsub.image.fits', maskmap=mask)
cube44.cube *= (13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['fourfour']/1e9))**2 *
1./cube44.header.get('BMAJ')/3600. * 1./cube44.header.get('BMIN')/3600. )
cube44.units = "K"
# Compute an error map. We use the 1-1 errors for all 3 because they're
# essentially the same, but you could use a different error map for each
# frequency
errmap11 = (pyfits.getdata('hotclump_11.cube_r0.5_rerun.image.moment_linefree.fits').squeeze()
* 13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['oneone']/1e9))**2 *
1./cube11.header.get('BMAJ')/3600. * 1./cube11.header.get('BMIN')/3600.
)
errmap11[errmap11 != errmap11] = agpy.smooth(errmap11, interp_nan=True)[errmap11 != errmap11]
# Stack the cubes into one big cube. The X-axis is no longer linear: there
# will be jumps from 1-1 to 2-2 to 4-4.
cubes = pyspeckit.CubeStack([cube11,cube22,cube44], maskmap=mask)
cubes.units = "K"
# Make a "moment map" to contain the initial guesses
# If you've already fit the cube, just re-load the saved version
# otherwise, re-fit it
if os.path.exists('hot_momentcube.fits'):
momentcubefile = pyfits.open('hot_momentcube.fits')
momentcube = momentcubefile[0].data
else:
cube11.mapplot()
# compute the moment at each pixel
from agpy import convolve, smooth
from pylab import *
test_image = zeros([512, 512])
# point
test_image[204, 204] = 1
# box
test_image[280:297, 280:297] = 1 / 256.
shiftest_image = zeros([300, 320])
shiftest_image[100:120, 180:220] = 1.0
print "Testing for a shift"
figure(11)
clf()
smoothed, kernel = smooth(shiftest_image, return_kernel=True)
subplot(221)
title("shiftest_image")
imshow(shiftest_image)
subplot(222)
title("smoothed")
imshow(smoothed)
subplot(223)
title("shiftest_image-smoothed")
imshow(shiftest_image - smoothed)
subplot(224)
title("kernel")
imshow(kernel)
"""
figure(0)
clf()
from agpy import convolve, smooth
from pylab import *
test_image = zeros([512, 512])
# point
test_image[204, 204] = 1
# box
test_image[280:297, 280:297] = 1 / 256.0
shiftest_image = zeros([300, 320])
shiftest_image[100:120, 180:220] = 1.0
print "Testing for a shift"
figure(11)
clf()
smoothed, kernel = smooth(shiftest_image, return_kernel=True)
subplot(221)
title("shiftest_image")
imshow(shiftest_image)
subplot(222)
title("smoothed")
imshow(smoothed)
subplot(223)
title("shiftest_image-smoothed")
imshow(shiftest_image - smoothed)
subplot(224)
title("kernel")
imshow(kernel)
"""
#tau2 = -np.log((spec2/cont2))
tau2 = -np.log((spec2s/cont2s))
tau2[np.isnan(tau2)] = 5
f2[0].header['CRPIX3'] -= 190
f2[0].data = tau2
f2.writeto('H2CO_22_Ku_BD_small_taucube_sm.fits',clobber=True)
spec2s += contlev+0.05
#tau2 = -np.log((spec2/cont2))
tau2 = -np.log((spec2s/cont2s))
tau2[np.isnan(tau2)] = 5
f2[0].header['CRPIX3'] -= 190
f2[0].data = tau2
f2.writeto('H2CO_22_Ku_BD_small_taucube_sm.fits',clobber=True)
get_ipython().magic(u'run -i ~/repos/w51evlareductionscripts/make_taucube_vla_ku_bd_22.py')
cont2s = agpy.smooth(cont2+contlev,kernelwidth=2)
tau2 = -np.log((spec2s/cont2s))
tau2[np.isnan(tau2)] = 5
f2[0].header['CRPIX3'] -= 190
f2[0].data = tau2
f2.writeto('H2CO_22_Ku_BD_small_taucube_sm.fits',clobber=True)
get_ipython().magic(u'run -i ~/repos/w51evlareductionscripts/make_taucube_vla_ku_bd_22.py')
get_ipython().magic(u'paste')
yy,xx = np.indices(tau2.shape[1:])
rr = ((xx-255.)**2+(yy-255.)**2)**0.5
mask = rr < 200
nanmask = np.zeros_like(mask,dtype='float')
nanmask[True-mask] = np.nan
integ1 = tau2[11:48,:,:].sum(axis=0)
f2[0].data = integ1 + nanmask
