def make_regions(filename="OMC1_TSPEC_H2S1_cube.fits"):
header = fits.getheader(filename)
with open("outflow_traces_pixels.reg", "w") as f:
print >> f, 'global color=white dashlist=8 3 width=1 font="helvetica 14 bold roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1'
print >> f, "image"
for ii, (ex, ey) in enumerate(outflow_endpoints):
print >> f, "line(%i,%i,%i,%i)" % (sourceI[0], sourceI[1], ex, ey)
print >> f, " # text(%f,%f) text={%i}" % (ex, ey, ii)
w = wcs.WCS(cubes.flatten_header(header))
with open("outflow_traces_fk5.reg", "w") as f:
print >> f, 'global color=white dashlist=8 3 width=1 font="helvetica 14 bold roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1'
print >> f, "fk5"
raI, decI = w.wcs_pix2world(np.array([sourceI], dtype="float"), 1)[0]
for ii, (ex, ey) in enumerate(outflow_endpoints):
ra, dec = w.wcs_pix2world(np.array([[ex, ey]], dtype="float"), 1)[0]
print >> f, "line(%f,%f,%f,%f)" % (raI, decI, ra, dec)
print >> f, " # text(%f,%f) text={%i}" % (ra, dec, ii)
s.attr[1]['color'] == color])
F.show_lines([coords.T], color=color)
F.add_scalebar(((10*u.pc)/(5.1*u.kpc)*u.radian).to(u.deg).value)
F.scalebar.set_label("10 pc")
F.scalebar.set_color((0.8,0.3,0.01,0.9))
F.scalebar.set_linewidth(3)
F.save(figpath+'W51_PVDiagrams_PVmap.pdf')
############################
# New stuff added 12/12/2013
############################
co32 = fits.open(datapath+'w51_bieging_13co32.fits')
cohdr = flatten_header(co32[0].header)
co_45to55 = co32[0].data[31:51,:,:].sum(axis=0)
co_55to60 = co32[0].data[51:61,:,:].sum(axis=0)
co_60to65 = co32[0].data[61:71,:,:].sum(axis=0)
co_65to75 = co32[0].data[71:91,:,:].sum(axis=0)
F.show_contour(fits.PrimaryHDU(co_45to55, cohdr), levels=[15,55,85,500],
filled=True,
colors=[(0,0.5,0.5,0.3),(0,0.5,0.5,0.4),(0,0.5,0.5,0.5)])
F.save(figpath+'w51_bgpsgrayscale_cooverlay_45to55.png')
for L in F._layers.keys():
if 'contour' in L:
F.remove_layer(L)
F.show_contour(fits.PrimaryHDU(co_55to60, cohdr), levels=[15,55,85,500],
filled=True, colors=[(0,0,1,0.2),(0,0,1,0.3),(0,0,1,0.4)])
def fit_powerspectra(fn,
psdsize=32,
nanthreshold=0.05,
doplot=False,
dowait=False,
largescalecutoff=250,
fwhm=33,
subdir='/powerspectra/'):
data = pyfits.getdata(fn)
header = pyfits.getheader(fn)
wcs = pywcs.WCS(cubes.flatten_header(header), )
savdir = os.path.split(fn)[0] + subdir
savname = os.path.split(fn)[1]
if data.shape[0] < psdsize or data.shape[1] < psdsize: return
lmin, bmin = wcs.wcs_pix2sky(0., 0., 0)
lmax, bmax = wcs.wcs_pix2sky(data.shape[1], data.shape[0], 0)
if hasattr(wcs.wcs, 'cd'): cdelt = wcs.wcs.cd[1, 1]
else: cdelt = wcs.wcs.cdelt[1]
if lmin > lmax: lmin, lmax = lmax, lmin
if bmin > bmax: bmin, bmax = bmax, bmin
lonlength_pix = (lmax - lmin) / cdelt
latlength_pix = (bmax - bmin) / cdelt
number_lon = np.floor(lonlength_pix[0] / psdsize)
number_lat = np.floor(latlength_pix[0] / psdsize)
modulus_lon = data.shape[1] % psdsize
modulus_lat = data.shape[0] % psdsize
powerlaw_fit_grid = np.zeros([number_lat, number_lon])
angle_grid = np.zeros([3, number_lat, number_lon])
powerspec_grid = np.zeros(
[np.round((psdsize - 1.0) / np.sqrt(2)) + 1, number_lat, number_lon])
anglespec_grid = np.zeros([12, number_lat, number_lon])
nskips = 0
for ll in range(number_lon):
for bb in range(number_lat):
bcen = bmin + (modulus_lat / 2. + psdsize / 2 +
psdsize * bb) * cdelt
lcen = lmin + (modulus_lon / 2. + psdsize / 2 +
psdsize * ll) * cdelt
lcen_pix, bcen_pix = wcs.wcs_sky2pix(lcen, bcen, 0)
lcen_pix = np.round(lcen_pix)[0]
bcen_pix = np.round(bcen_pix)[0]
subimage = data[bcen_pix - psdsize / 2:bcen_pix + psdsize / 2,
lcen_pix - psdsize / 2:lcen_pix + psdsize / 2]
if (subimage.shape[0] > 0 and subimage.shape[1] > 0):
subimage2 = np.zeros([psdsize, psdsize]) + np.nan
subimage2[:subimage.shape[0], :subimage.shape[1]] = subimage
subimage = subimage2
if np.isnan(subimage).sum() / float(psdsize**2) > nanthreshold:
print "Skipping position %f,%f because nan %% = %5.1f" % (
lcen, bcen,
np.isnan(subimage).sum() / float(psdsize**2) * 100.0)
nskips += 1
continue
if subimage.shape[0] != subimage.shape[1] or subimage.shape[
0] == 0 or subimage.shape[1] == 0:
print "Skipping position %f,%f because image dimensions are asymmetric." % (
lcen, bcen)
nskips += 1
continue
#print "Computing PSD. np.nansum(subimage) = %g" % np.nansum(subimage)
rr, zz = psds.power_spectrum(subimage)
rr_as = (cdelt * 3600.) / rr
OK = rr_as < largescalecutoff
(scale1, scale2, breakpoint, pow1,
pow2), mpf = powerfit.brokenpowerfit(rr[OK],
zz[OK],
breakpoint=0.23,
alphaguess1=-2.0,
alphaguess2=0.0,
scaleguess=np.median(zz))
params = mpf.params
perror = mpf.perror
rmax = np.min([(rr_as < fwhm).argmax(),
(rr > breakpoint).argmax()])
rmin = np.max([2, (rr_as > largescalecutoff).argmax()])
az, zaz = psds.power_spectrum(subimage - subimage.mean(),
radial=True,
radbins=np.array([rmin, rmax]),
binsize=30.0)
mpangle = sinfit(az, zaz)
angle_grid[:, bb, number_lon - 1 - ll] = mpangle.params
anglespec_grid[:, bb, number_lon - 1 - ll] = zaz
powerlaw_fit_grid[bb, number_lon - 1 - ll] = pow1
powerspec_grid[:, bb, number_lon - 1 - ll] = zz
print "Position %7.3g,%7.3g has mean %8.2g and fit parameters %8.2g,%8.2g,%8.2g,%8.2g,%8.2g and angles %8.2g,%8.2g,%8.2g" % (
lcen, bcen, subimage.mean(), scale1, scale2, breakpoint, pow1,
pow2, mpangle.params[0], mpangle.params[1], mpangle.params[2])
#print " %5.2g,%5.2g,%5.2g,%5.2g,%5.2g" % (perror[0],perror[0],perror[1],perror[2],perror[3])
if doplot:
pylab.figure(1)
pylab.clf()
pylab.loglog(rr, zz, 'gray')
pylab.loglog(rr[OK], zz[OK], 'k')
pylab.plot(
rr,
scale1 * rr**pow1 * (rr < breakpoint) + scale2 * rr**pow2 *
(rr >= breakpoint))
pylab.annotate("p1 = %8.3f" % pow1, [0.75, 0.85],
xycoords='figure fraction')
pylab.annotate("p2 = %8.3f" % pow2, [0.75, 0.80],
xycoords='figure fraction')
pylab.annotate("break = %8.3f" % breakpoint, [0.75, 0.75],
xycoords='figure fraction')
pylab.draw()
if dowait: raw_input("WAIT")
if ll == 0 and bb == 0: return
if nskips >= number_lat * number_lon: return
bcen = bmin + (modulus_lat / 2. + psdsize / 2) * cdelt
lcen = lmin + (modulus_lon / 2. + psdsize / 2) * cdelt
new_cdelt = cdelt * psdsize
header.update('CD1_1', -1 * new_cdelt)
header.update('CD2_2', new_cdelt)
header.update('CRPIX1', number_lon)
header.update('CRPIX2', 1.0)
#lcen,bcen = wcs.wcs_pix2sky(np.floor(number_lon)/2.*psdsize,np.floor(number_lat)/2.*psdsize,1)
header.update('CRVAL1', lcen[0])
header.update('CRVAL2', bcen[0])
newHDU_powerfit = pyfits.PrimaryHDU(powerlaw_fit_grid, header=header)
newHDU_powerfit.writeto(savdir + savname +
"_powerlaw_fit_grid_%i.fits" % psdsize,
clobber=True)
newHDU_powerfit = pyfits.PrimaryHDU(angle_grid, header=header)
newHDU_powerfit.writeto(savdir + savname +
"_angle_fit_grid_%i.fits" % psdsize,
clobber=True)
header.update('CD3_3', np.median(rr[1:] - rr[:-1]))
header.update('CRVAL3', rr[0])
header.update('CRPIX3', 1.0)
header.update('CUNIT3', 'Jy^2')
header.update('CTYPE3', 'PowerSpec')
newHDU = pyfits.PrimaryHDU(powerspec_grid, header=header)
newHDU.writeto(savdir + savname + "_powerspec_grid_%i.fits" % psdsize,
clobber=True)
header.update('CD3_3', np.median(az[1:] - az[:-1]))
header.update('CRVAL3', az[0])
header.update('CUNIT3', 'Jy^2')
header.update('CTYPE3', 'AngularPowerSpec')
newHDU_powerfit = pyfits.PrimaryHDU(anglespec_grid, header=header)
newHDU_powerfit.writeto(savdir + savname +
"_anglespec_grid_%i.fits" % psdsize,
clobber=True)
fig = pylab.figure(2)
pylab.clf()
pylab.spectral()
ax = pylab.subplot(121)
pylab.imshow(np.arcsinh(data))
#pylab.imshow(np.log10(data-np.nanmin(data)+1),vmin=-1,vmax=1)
ax.xaxis.set_major_locator(OffsetMultipleLocator(psdsize,
modulus_lon / 2.))
ax.yaxis.set_major_locator(OffsetMultipleLocator(psdsize,
modulus_lat / 2.))
ax.xaxis.grid(True, 'major')
ax.yaxis.grid(True, 'major')
pylab.subplot(122)
pylab.imshow(powerlaw_fit_grid,
vmin=-7,
vmax=0,
extent=[
0, powerlaw_fit_grid.shape[1] * psdsize, 0,
powerlaw_fit_grid.shape[0] * psdsize
])
cax = pylab.axes([0.9225, 0.1, 0.020, 0.80], axisbg='w', frameon=False)
pylab.colorbar(cax=cax)
pylab.savefig(savdir + savname + "_powerlaw_fit_grid_%i.png" % (psdsize))
