def make_rprof(regions, ploteach=False):
names = [r.attr[1]['text'] for r in regions]
center_positions = coordinates.SkyCoord([r.coord_list
for r in regions],
unit=(u.deg, u.deg),
frame='fk5')
size = u.Quantity([1.25,1.25], u.arcsec)
if ploteach:
nplots = len(names)
for ii in range(nplots):
pl.figure(ii).clf()
pl.figure(nplots+ii).clf()
pl.figure(nplots*2+ii).clf()
linestyles = {name: itertools.cycle(['-'] + ['--'] + [':'] + ['-.'])
for name in names}
for fn in ffiles:
fh = fits.open(paths.dpath("12m/continuum/"+fn))
mywcs = wcs.WCS(fh[0].header)
if 'BMAJ' not in fh[0].header:
#print("File {0} does not have BMAJ".format(fn))
continue
try:
beam = radio_beam.Beam.from_fits_header(fh[0].header)
except KeyError:
#print("File {0} doesn't have beam info in the header".format(fn))
continue
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
ppbeam = (beam.sr/(pixscale**2*u.deg**2)).decompose().value / u.beam
#print("fn {0} ppbeam={1:0.2f}".format(fn, ppbeam))
for ii,(name,position) in enumerate(zip(names, center_positions)):
cutout = Cutout2D(fh[0].data, position, size, wcs=mywcs)
nr, bins, rprof = azimuthalAverage(cutout.data, binsize=1.0,
return_nr=True)
linestyle = next(linestyles[name])
pl.figure(ii)
pl.title(name)
pl.plot(bins*pixscale*3600., rprof/ppbeam,
label=fn.split(".")[0], linestyle=linestyle)
pl.ylabel("Azimuthally Averaged Flux (Jy)")
pl.xlabel("Radius [arcsec]")
cumul_rprof = np.nan_to_num(rprof*nr/ppbeam).cumsum()
pl.figure(nplots+ii)
pl.title(name)
pl.plot(bins*pixscale*3600., cumul_rprof,
label=fn.split(".")[0], linestyle=linestyle)
pl.ylabel("Cumulative Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if ii == 0:
ax = pl.gca()
ax2 = ax.twiny()
ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.pc, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = [0.005,0.01,0.015,0.02,0.025]*u.pc
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius (pc)")
ax3.set_ylim(ax.get_ylim())
yticks_mass = np.arange(0,6000,1000)
yticks_Jy = yticks_mass/masscalc.mass_conversion_factor().value
ax3.set_yticks(yticks_Jy)
ax3.set_yticklabels(yticks_mass)
ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=20$ K)")
pl.figure(nplots*2+ii)
pl.title(name)
pl.plot(((bins*pixscale*u.deg)*masscalc.distance).to(u.pc,
u.dimensionless_angles()),
cumul_rprof * masscalc.mass_conversion_factor(),
label=fn.split(".")[0], linestyle=linestyle)
pl.ylabel("Cumulative Mass (M$_\\odot$, $T=20$ K)")
pl.xlabel("Radius (pc)")
for ii in range(nplots):
for xtra in (0,nplots*2):
ax = pl.figure(ii+xtra).gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
else:
nplots = 0
pl.matplotlib.rc_file('pubfiguresrc')
for jj in range(nplots*3+1, nplots*3+11):
pl.figure(jj).clf()
# $ find ~/work/w51/alma/FITS/ -samefile ~/work/w51/alma/FITS/W51_te_continuum_best.fits
# /Users/adam/work/w51/alma/FITS//12m/continuum/selfcal_allspw_selfcal_3_mfs_deeper.image.pbcor.fits
# /Users/adam/work/w51/alma/FITS//W51_te_continuum_best.fits
fn = "selfcal_allspw_selfcal_3_mfs_deeper.image.pbcor.fits"
fh = fits.open(paths.dpath("12m/continuum/"+fn))
mywcs = wcs.WCS(fh[0].header)
beam = radio_beam.Beam.from_fits_header(fh[0].header)
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
ppbeam = (beam.sr/(pixscale**2*u.deg**2)).decompose().value / u.beam
for ii,(name,position) in enumerate(zip(names, center_positions)):
cutout = Cutout2D(fh[0].data, position, size, wcs=mywcs)
nr, bins, rprof = azimuthalAverage(cutout.data, binsize=1.0,
return_nr=True)
pl.figure(nplots*3+1)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., rprof/ppbeam,
label=name)
pl.ylabel("Azimuthally Averaged Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
cumul_rprof = np.nan_to_num(rprof*nr/ppbeam).cumsum()
pl.figure(nplots*3+2)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., cumul_rprof,
label=name)
pl.ylabel("Cumulative Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax2 = ax.twiny()
ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
ax3.set_ylim(ax.get_ylim())
yticks_mass = np.arange(0,6000,1000)
yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
ax3.set_yticks(yticks_Jy)
ax3.set_yticklabels(yticks_mass)
ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
radii = ((bins*pixscale*u.deg)*masscalc.distance).to(u.pc,
u.dimensionless_angles())
mass_40k_profile = (cumul_rprof * masscalc.mass_conversion_factor(TK=40) / u.beam).to(u.M_sun)
pl.figure(nplots*3+3)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(radii, mass_40k_profile, label=name)
pl.ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.xlabel("Radius (pc)")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
density_40k_profile = (mass_40k_profile / (4/3.*np.pi*radii**3) / (2.8*u.Da)).to(u.cm**-3)
#print(density_40k_profile)
#print(radii)
pl.figure(nplots*3+4)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.semilogy(radii, density_40k_profile, label=name)
pl.ylabel("Cumulative Density [n(H$_2$), $T=40$ K]")
pl.xlabel("Radius (pc)")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
angular_radii = bins*pixscale*3600.
azimuthal_average_flux = rprof/ppbeam
azimuthal_average_mass = azimuthal_average_flux * masscalc.mass_conversion_factor(TK=40) / u.beam
bindiff_as = np.diff(bins).mean() * pixscale * 3600.
bindiff_cm = bindiff_as * masscalc.distance / 206265.
bins_cm = (angular_radii * masscalc.distance / 206265.).to(u.cm)
sqdiffbins_cm = u.Quantity([bins_cm[0].to(u.cm).value**2] +
(bins_cm.to(u.cm)[1:]**2 -
bins_cm.to(u.cm)[:-1]**2).value.tolist(),
u.cm**2)
cudiffbins_cm = u.Quantity([bins_cm[0].to(u.cm).value**3] +
(bins_cm.to(u.cm)[1:]**3 -
bins_cm.to(u.cm)[:-1]**3).value.tolist(),
u.cm**3)
sqdiffbins_pix = [bins[0]**2] + (bins[1:]**2 - bins[:-1]**2).tolist()
azimuthal_average_density = (azimuthal_average_mass * sqdiffbins_pix /
(2.8*u.Da) /
(4/3.*np.pi*(cudiffbins_cm))).to(u.cm**-3)
pl.figure(nplots*3+5)
pl.semilogy(angular_radii, azimuthal_average_density, label=name)
pl.ylabel("Azimuthally Averaged Density [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
c_s_40k = ((constants.k_B * 40*u.K / (2.4*u.Da))**0.5).to(u.km/u.s)
azimuthal_average_MJ_40k = (np.pi/6. * c_s_40k**3 /
(constants.G**1.5 *
(2.8*u.Da*azimuthal_average_density)**0.5)).to(u.M_sun)
pl.figure(nplots*3+6)
pl.semilogy(angular_radii, azimuthal_average_MJ_40k, label=name)
pl.ylabel("Azimuthally Averaged $M_J$ at $T=40$K")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
source = 'e2' if name == 'e2e' else name
temperature_map_fn = paths.dpath('12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(source))
temperature_map_fh = fits.open(temperature_map_fn)
# this whole section is copied from overlay_contours_on_ch3oh
ch3ohN_hdul = fits.open(paths.dpath('12m/moments/CH3OH_{0}_cutout_columnmap.fits'.format(source)))
ch3ohT_hdul = fits.open(paths.dpath('12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(source)))
bigwcs = wcs.WCS(ch3ohT_hdul[0].header)
bigpixscale = (bigwcs.pixel_scale_matrix.diagonal()**2).sum()**0.5 * u.deg
ch3ohN = ch3ohN_hdul[0].data
ch3ohT = ch3ohT_hdul[0].data
dust_brightness,wts = reproject.reproject_interp(fits.open(paths.dpath('W51_te_continuum_best.fits')),
ch3ohN_hdul[0].header)
bm = radio_beam.Beam.from_fits_header(paths.dpath("W51_te_continuum_best.fits"))
yy,xx = np.indices(ch3ohN.shape)
if source == 'north':
center = [84.,38.]
else:
center = [ch3ohN.shape[0]/2., ch3ohN.shape[1]/2.]
yyc = (yy-center[0])
xxc = (xx-center[1])
rr = (yyc**2 + xxc**2)**0.5
rr_as = (rr*bigpixscale).to(u.arcsec)
theta = np.arctan2(yyc,xxc)*u.rad
dust_column = dust_emissivity.dust.colofsnu(225*u.GHz, dust_brightness*u.Jy,
beamomega=bm,
temperature=ch3ohT*u.K)
ch3oh_abundance = ch3ohN / dust_column.value
mask = (ch3oh_abundance > 1e-10) & (ch3oh_abundance < 1e-5)
if source == 'e2':
mask = mask & (((theta > 15*u.deg) & (theta < 345*u.deg)) | (theta < -15*u.deg))
mask = mask & np.isfinite(ch3oh_abundance)
# exclude high-abundance, low-column regions: likely to be div-by-zero zones
mask = mask & (~((ch3ohN < 1e18) & (ch3oh_abundance > 5e-6)))
mask = mask & (~((dust_brightness<1e-2) & (ch3ohT > 500) & (ch3oh_abundance > 1e-6)))
#mask = mask & (~((ch3ohT > 250) &
# (ch3ohN < 1e18) &
# (rr_as>1.5*u.arcsec))
# )# these are low-column,
temwcs = wcs.WCS(temperature_map_fh[0].header)
temcutout = Cutout2D(temperature_map_fh[0].data, position, size, wcs=temwcs)
maskcutout = Cutout2D(mask.astype('float'), position, size, wcs=bigwcs)
tem_nr, tem_bins, tem_rprof = azimuthalAverage(temcutout.data,
weights=(maskcutout.data>0).astype('float'),
binsize=1.0,
return_nr=True,
interpnan=True,
)
mass_profile = (cumul_rprof * masscalc.mass_conversion_factor(TK=tem_rprof) / u.beam).to(u.M_sun)
density_profile = (mass_profile / (4/3.*np.pi*radii**3) / (2.8*u.Da)).to(u.cm**-3)
density_alpha = ((np.log(density_profile[1:].value) -
np.log(density_profile[:-1].value)) /
(np.log(angular_radii[1:]) -
np.log(angular_radii[:-1]))
)
c_s = ((constants.k_B * tem_rprof*u.K / (2.4*u.Da))**0.5).to(u.km/u.s)
azimuthal_average_MJ = (np.pi/6. * c_s**3 /
(constants.G**1.5 *
(2.8*u.Da*azimuthal_average_density)**0.5)
).to(u.M_sun)
pl.figure(nplots*3+7)
pl.plot(angular_radii, azimuthal_average_MJ, label=name)
pl.ylabel("Azimuthally Averaged $M_J$ at $T(\\mathrm{CH}_3\\mathrm{OH})$")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots*3+8)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., mass_profile,
label=name)
pl.ylabel("Cumulative Mass at T(CH$_3$OH)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='upper left')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax2 = ax.twiny()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots*3+9)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., tem_rprof,
label=name)
pl.legend(loc='best')
pl.ylabel("CH$_3$OH temperature")
pl.xlabel("Radius [as]")
azimuthal_average_RJ = (c_s**1 /
(constants.G**0.5 *
(2.8*u.Da*azimuthal_average_density)**0.5)
).to(u.au)
pl.figure(nplots*3+10)
pl.plot(angular_radii, azimuthal_average_RJ, label=name)
pl.plot([0,(7000*u.au/masscalc.distance).to(u.arcsec,
u.dimensionless_angles()).value],
[0,7000], 'k--', alpha=0.5)
pl.ylabel("Azimuthally Averaged $R_J$ at $T(\\mathrm{CH}_3\\mathrm{OH})$ [au]")
pl.xlabel("Radius [arcsec]")
pl.ylim(0,3000)
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots*3+11)
pl.plot((angular_radii[1:]+angular_radii[:-1])/2., density_alpha)
pl.xlabel("Radius [as]")
pl.ylabel("Density Profile Exponent $\\kappa_\\rho$")
# Image shape
nax2, nax1 = model.shape
# High scale factor
highresscalefactor = 1.0
# Low scale factor
lowresscalefactor = 1.0
# x-axis unit
xaxisunit = "lambda"
sd_kernel = sd_beam.as_kernel(pixscale, x_size=nax2, y_size=nax1)
kfft = np.abs(np.fft.fft2(sd_kernel))
ikfft = 1 - kfft
rad, azavg_kernel = azimuthalAverage(np.abs(kfft), returnradii=True)
rad, azavg_ikernel = azimuthalAverage(np.abs(ikfft), returnradii=True)
fft_lo = np.fft.fftshift(np.fft.fft2(np.nan_to_num(model * lowresscalefactor)))
folders = glob(append_path("14B-088_HI_LSRK.ms.contsub_channel_1000.CASAVer*"))
# Values to extract out.
for folder in folders:
# Slice out the first 3 since this is just the name
split_name = folder.split(".")[3:]
# Now try opening the image, if it exists
imagename = os.path.join(folder, os.path.basename(folder) + ".clean.image.fits")
feathered_imagename = os.path.join(folder, os.path.basename(folder) + ".clean.image.feathered.fits")
plt.imshow(merged, origin='lower')
# Compare power spectra.
high_fft = np.fft.fft2(np.nan_to_num(high_data))
low_fft = np.fft.fft2(np.nan_to_num(low_data))
# merged[np.isnan(high_hdu.data)] = np.NaN
merged_fft = np.fft.fft2(np.nan_to_num(merged))
# And the CASA feather version
# casa_fft = np.fft.fft2(np.nan_to_num(high_hdu_casa_feather.data))
rad, azavg_sum = azimuthalAverage(np.abs(np.fft.fftshift(merged_fft)),
returnradii=True)
azavg_high = azimuthalAverage(np.abs(np.fft.fftshift(high_fft)))
azavg_low = azimuthalAverage(np.abs(np.fft.fftshift(low_fft)))
# azavg_casa = azimuthalAverage(np.abs(np.fft.fftshift(casa_fft)))
rad_pix = 2560. / rad
xaxis = pixscale.to(u.arcsec).value * rad_pix
ax1 = plt.subplot(1, 1, 1)
ax1.loglog(xaxis, azavg_high, color='b', linewidth=3,
alpha=0.5,
linestyle='-.',
label="VLA")
ax1.loglog(xaxis, azavg_low, color='r', linewidth=2,
regrid_mask[np.isnan(regrid_mask)] = 0.0
regrid_mask = regrid_mask == 1.0
regrid_mask = nd.binary_fill_holes(regrid_mask)
low_hdu_orig.data[~regrid_mask] = np.NaN
# regrid_model[~mask[::-1, ::-1]] = np.NaN
fft_sd_arecibo = np.fft.fftshift(np.fft.fft2(np.nan_to_num(low_hdu_orig.data)))
pixscale_arecibo = \
wcs.utils.proj_plane_pixel_area(wcs.WCS(low_hdu_orig))**0.5 * \
u.deg
# SD Azimuthal average
rad, azavg_sd = azimuthalAverage(np.abs(fft_sd),
returnradii=True)
rad_arecibo, azavg_sd_arecibo = \
azimuthalAverage(np.abs(fft_sd_arecibo), returnradii=True)
# rad_model, azavg_sd_model = \
# azimuthalAverage(np.abs(np.fft.fftshift(np.fft.fft2(np.nan_to_num(regrid_model)))), returnradii=True)
# azavg_sd_model_fitstools = \
# azimuthalAverage(np.abs(np.fft.fftshift(np.fft.fft2(np.nan_to_num(regrid_model_fitstools)))))
# Grid scaling used.
pixscale = 3 * u.arcsec
# Image shape
nax2, nax1 = model.shape
# High scale factor
highresscalefactor = 1.0
def make_rprof(regions, ploteach=False):
names = [r.attr[1]['text'] for r in regions]
center_positions = coordinates.SkyCoord([r.coord_list for r in regions],
unit=(u.deg, u.deg),
frame='fk5')
size = u.Quantity([1.25, 1.25], u.arcsec)
if ploteach:
nplots = len(names)
for ii in range(nplots):
pl.figure(ii, figsize=figsize).clf()
pl.figure(nplots + ii, figsize=figsize).clf()
pl.figure(nplots * 2 + ii, figsize=figsize).clf()
linestyles = {
name: itertools.cycle(['-'] + ['--'] + [':'] + ['-.'])
for name in names
}
for fn in ffiles:
fh = fits.open(paths.dpath("12m/continuum/" + fn))
mywcs = wcs.WCS(fh[0].header)
if 'BMAJ' not in fh[0].header:
#print("File {0} does not have BMAJ".format(fn))
continue
try:
beam = radio_beam.Beam.from_fits_header(fh[0].header)
except KeyError:
#print("File {0} doesn't have beam info in the header".format(fn))
continue
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
ppbeam = (beam.sr /
(pixscale**2 * u.deg**2)).decompose().value / u.beam
#print("fn {0} ppbeam={1:0.2f}".format(fn, ppbeam))
for jj, (name, position) in enumerate(zip(names,
center_positions)):
cutout = Cutout2D(fh[0].data, position, size, wcs=mywcs)
nr, bins, rprof = azimuthalAverage(cutout.data,
binsize=1.0,
return_nr=True)
linestyle = next(linestyles[name])
pl.figure(jj, figsize=figsize)
pl.title(name)
pl.plot(bins * pixscale * 3600.,
rprof / ppbeam,
label=fn.split(".")[0],
linestyle=linestyle)
pl.ylabel("Azimuthally Averaged Flux (Jy)")
pl.xlabel("Radius [arcsec]")
cumul_rprof = np.nan_to_num(rprof * nr / ppbeam).cumsum()
pl.figure(nplots + jj, figsize=figsize)
pl.title(name)
pl.plot(bins * pixscale * 3600.,
cumul_rprof,
label=fn.split(".")[0],
linestyle=linestyle)
if jj == 0:
pl.fill_between([0, beam.major.to(u.arcsec).value],
[100, 100], [0, 0],
zorder=-5,
alpha=0.1,
color='k')
pl.ylabel("Cumulative Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if jj == 0:
ax = pl.gca()
ax2 = ax.twiny()
ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.pc, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = [0.005, 0.01, 0.015, 0.02, 0.025
] * u.pc
new_tick_locs_as = (new_tick_locations /
masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius (pc)")
ax3.set_ylim(ax.get_ylim())
yticks_mass = np.arange(0, 6000, 1000)
yticks_Jy = yticks_mass / masscalc.mass_conversion_factor(
).value
ax3.set_yticks(yticks_Jy)
ax3.set_yticklabels(yticks_mass)
ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=20$ K)")
pl.figure(nplots * 2 + jj, figsize=figsize)
pl.title(name)
pl.plot(((bins * pixscale * u.deg) * masscalc.distance).to(
u.pc, u.dimensionless_angles()),
cumul_rprof * masscalc.mass_conversion_factor(),
label=fn.split(".")[0],
linestyle=linestyle)
if jj == 0:
pl.fill_between([0, beam.major.to(u.arcsec).value],
[100, 100], [0, 0],
zorder=-5,
alpha=0.1,
color='k')
pl.ylabel("Cumulative Mass (M$_\\odot$, $T=20$ K)")
pl.xlabel("Radius (pc)")
for ii in range(nplots):
for xtra in (0, nplots * 2):
ax = pl.figure(ii + xtra, figsize=figsize).gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
else:
nplots = 0
for jj in range(nplots * 3 + 1, nplots * 3 + 15 + 1):
pl.figure(jj, figsize=figsize).clf()
# $ find ~/work/w51/alma/FITS/ -samefile ~/work/w51/alma/FITS/W51_te_continuum_best.fits
# /Users/adam/work/w51/alma/FITS//12m/continuum/selfcal_allspw_selfcal_3_mfs_deeper.image.pbcor.fits
# /Users/adam/work/w51/alma/FITS//W51_te_continuum_best.fits
fn = "selfcal_allspw_selfcal_3_mfs_deeper.image.pbcor.fits"
fh = fits.open(paths.dpath("12m/continuum/" + fn))
mywcs = wcs.WCS(fh[0].header)
beam = radio_beam.Beam.from_fits_header(fh[0].header)
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
ppbeam = (beam.sr / (pixscale**2 * u.deg**2)).decompose().value / u.beam
for ii, (name, position) in enumerate(zip(names, center_positions)):
cutout = Cutout2D(fh[0].data, position, size, wcs=mywcs)
nr, bins, rprof = azimuthalAverage(cutout.data,
binsize=1.0,
return_nr=True)
pl.figure(nplots * 3 + 1, figsize=figsize)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins * pixscale * 3600., rprof / ppbeam, label=name)
pl.ylabel("Azimuthally Averaged Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
cumul_rprof = np.nan_to_num(rprof * nr / ppbeam).cumsum()
pl.figure(nplots * 3 + 2, figsize=figsize)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins * pixscale * 3600., cumul_rprof, label=name)
pl.ylabel("Cumulative Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax2 = ax.twiny()
ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
ax3.set_ylim(ax.get_ylim())
yticks_mass = np.arange(0, 6000, 1000)
yticks_Jy = yticks_mass / masscalc.mass_conversion_factor(
TK=40).value
ax3.set_yticks(yticks_Jy)
ax3.set_yticklabels(yticks_mass)
ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
radii = ((bins * pixscale * u.deg) * masscalc.distance).to(
u.pc, u.dimensionless_angles())
mass_40k_profile = (cumul_rprof *
masscalc.mass_conversion_factor(TK=40) /
u.beam).to(u.M_sun)
pl.figure(nplots * 3 + 3, figsize=figsize)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(radii, mass_40k_profile, label=name)
pl.ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.xlabel("Radius (pc)")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
density_40k_profile = (mass_40k_profile / (4 / 3. * np.pi * radii**3) /
(2.8 * u.Da)).to(u.cm**-3)
#print(density_40k_profile)
#print(radii)
pl.figure(nplots * 3 + 4, figsize=figsize)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.semilogy(radii, density_40k_profile, label=name)
pl.ylabel("Cumulative Density [n(H$_2$), $T=40$ K]")
pl.xlabel("Radius (pc)")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
angular_radii = bins * pixscale * 3600.
azimuthal_average_flux = rprof / ppbeam
azimuthal_average_mass_40K = azimuthal_average_flux * masscalc.mass_conversion_factor(
TK=40) / u.beam
bindiff_as = np.diff(bins).mean() * pixscale * 3600.
bindiff_cm = bindiff_as * masscalc.distance / 206265.
bins_cm = (angular_radii * masscalc.distance / 206265.).to(u.cm)
sqdiffbins_cm = u.Quantity([bins_cm[0].to(u.cm).value**2] +
(bins_cm.to(u.cm)[1:]**2 -
bins_cm.to(u.cm)[:-1]**2).value.tolist(),
u.cm**2)
cudiffbins_cm = u.Quantity([bins_cm[0].to(u.cm).value**3] +
(bins_cm.to(u.cm)[1:]**3 -
bins_cm.to(u.cm)[:-1]**3).value.tolist(),
u.cm**3)
sqdiffbins_pix = [bins[0]**2] + (bins[1:]**2 - bins[:-1]**2).tolist()
azimuthal_average_density_40K = (azimuthal_average_mass_40K *
sqdiffbins_pix / (2.8 * u.Da) /
(4 / 3. * np.pi *
(cudiffbins_cm))).to(u.cm**-3)
pl.figure(nplots * 3 + 5, figsize=figsize)
pl.semilogy(angular_radii, azimuthal_average_density_40K, label=name)
pl.ylabel("Azimuthally Averaged Density\nat 40K [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
c_s_40k = ((constants.k_B * 40 * u.K / (2.4 * u.Da))**0.5).to(u.km /
u.s)
azimuthal_average_MJ_40k = (
np.pi / 6. * c_s_40k**3 /
(constants.G**1.5 *
(2.8 * u.Da * azimuthal_average_density_40K)**0.5)).to(u.M_sun)
pl.figure(nplots * 3 + 6, figsize=figsize)
pl.semilogy(angular_radii, azimuthal_average_MJ_40k, label=name)
pl.ylabel("Azimuthally Averaged $M_J$\nat $T=40$K")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
source = 'e2' if name == 'e2e' else name
temperature_map_fn = paths.dpath(
'12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(source))
temperature_map_fh = fits.open(temperature_map_fn)
# this whole section is copied from overlay_contours_on_ch3oh
ch3ohN_hdul = fits.open(
paths.dpath(
'12m/moments/CH3OH_{0}_cutout_columnmap.fits'.format(source)))
ch3ohT_hdul = fits.open(
paths.dpath(
'12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(
source)))
bigwcs = wcs.WCS(ch3ohT_hdul[0].header)
bigpixscale = (bigwcs.pixel_scale_matrix.diagonal()**
2).sum()**0.5 * u.deg
ch3ohN = ch3ohN_hdul[0].data
ch3ohT = ch3ohT_hdul[0].data
dust_brightness, wts = reproject.reproject_interp(
fits.open(paths.dpath('W51_te_continuum_best.fits')),
ch3ohN_hdul[0].header)
contbm = radio_beam.Beam.from_fits_header(
paths.dpath("W51_te_continuum_best.fits"))
yy, xx = np.indices(ch3ohN.shape)
if source == 'north':
center = [84., 38.]
else:
center = [ch3ohN.shape[0] / 2., ch3ohN.shape[1] / 2.]
yyc = (yy - center[0])
xxc = (xx - center[1])
rr = (yyc**2 + xxc**2)**0.5
rr_as = (rr * bigpixscale).to(u.arcsec)
theta = np.arctan2(yyc, xxc) * u.rad
dust_column = dust_emissivity.dust.colofsnu(
225 * u.GHz,
dust_brightness * u.Jy / contbm,
#beamomega=contbm,
temperature=ch3ohT * u.K)
ch3oh_abundance = ch3ohN / dust_column.value
mask = (ch3oh_abundance > 1e-10) & (ch3oh_abundance < 1e-5)
if source == 'e2':
mask = mask & (((theta > 15 * u.deg) & (theta < 345 * u.deg)) |
(theta < -15 * u.deg))
mask = mask & np.isfinite(ch3oh_abundance)
# exclude high-abundance, low-column regions: likely to be div-by-zero zones
mask = mask & (~((ch3ohN < 1e18) & (ch3oh_abundance > 5e-6)))
mask = mask & (~((dust_brightness < 1e-2) & (ch3ohT > 500) &
(ch3oh_abundance > 1e-6)))
#mask = mask & (~((ch3ohT > 250) &
# (ch3ohN < 1e18) &
# (rr_as>1.5*u.arcsec))
# )# these are low-column,
temwcs = wcs.WCS(temperature_map_fh[0].header)
temcutout = Cutout2D(temperature_map_fh[0].data,
position,
size,
wcs=temwcs)
maskcutout = Cutout2D(mask.astype('float'), position, size, wcs=bigwcs)
tem_nr, tem_bins, tem_rprof = azimuthalAverage(
temcutout.data,
weights=(maskcutout.data > 0).astype('float'),
binsize=1.0,
return_nr=True,
interpnan=True,
)
azimuthal_average_temperature = tem_rprof
mass_map = (cutout.data *
masscalc.mass_conversion_factor(TK=temcutout.data)).to(
u.M_sun)
yy, xx = np.indices(cutout.data.shape)
rr_map = ((xx - cutout.data.shape[1] / 2.)**2 +
(yy - cutout.data.shape[0] / 2.)**2)
mass_profile = (cumul_rprof *
masscalc.mass_conversion_factor(TK=tem_rprof) /
u.beam).to(u.M_sun)
density_profile = (mass_profile / (4 / 3. * np.pi * radii**3) /
(2.8 * u.Da)).to(u.cm**-3)
azimuthal_average_mass = (
azimuthal_average_flux *
masscalc.mass_conversion_factor(TK=tem_rprof) / u.beam)
# how accurate is this? We are measuring mass in cylindrical annuli,
# but we are assuming the underlying source structure is spherical
# See Cores.ipynb (in notes/, not in this repo) for a detailed
# analysis...
azimuthal_average_density = (azimuthal_average_mass * sqdiffbins_pix /
(2.8 * u.Da) / (4 / 3. * np.pi *
(cudiffbins_cm))).to(
u.cm**-3)
# this is not a good approximation for the spherial density profile...
# see cores.ipynb.
# Really should be this number +1 (so see the +1 below)
# Also, negatived...
density_alpha_ = 1 - (
(np.log(density_profile[1:].value) -
np.log(density_profile[:-1].value)) /
(np.log(angular_radii[1:]) - np.log(angular_radii[:-1])))
# from cores.ipynb, this actually gets (close to) the right answer
density_alpha = -(
(np.log(azimuthal_average_density[1:].value) -
np.log(azimuthal_average_density[:-1].value)) /
(np.log(angular_radii[1:]) - np.log(angular_radii[:-1])))
c_s = ((constants.k_B * tem_rprof * u.K / (2.4 * u.Da))**0.5).to(u.km /
u.s)
azimuthal_average_MJ = (
np.pi / 6. * c_s**3 /
(constants.G**1.5 *
(2.8 * u.Da * azimuthal_average_density)**0.5)).to(u.M_sun)
cumulative_mass_weighted_temperature = (
(azimuthal_average_mass * azimuthal_average_temperature).cumsum() /
mass_profile)
cumulative_c_s = ((constants.k_B *
cumulative_mass_weighted_temperature * u.K /
(2.4 * u.Da))**0.5).to(u.km / u.s)
cumulative_profile_MJ = (np.pi / 6. * cumulative_c_s**3 /
(constants.G**1.5 *
(2.8 * u.Da * density_profile)**0.5)).to(
u.M_sun)
pl.figure(nplots * 3 + 7, figsize=figsize)
line, = pl.plot(angular_radii, azimuthal_average_MJ, label=name)
# WRONG pl.plot(angular_radii, azimuthal_average_mass, linestyle='--',
# WRONG alpha=0.75, color=line.get_color())
#pl.plot(rr_map*pixscale*3600., mass_map, 'k.', alpha=0.25)
pl.axis([0, 1.2, 0.0, 5.0])
pl.ylabel(
"Azimuthally Averaged $M_J$\nat $T(\\mathrm{CH}_3\\mathrm{OH})$ [$M_\\odot$]"
)
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots * 3 + 8, figsize=figsize)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins * pixscale * 3600., mass_profile, label=name)
if ii == 0:
pl.fill_between([0, beam.major.to(u.arcsec).value], [100, 100],
[0, 0],
zorder=-5,
alpha=0.1,
color='k')
pl.ylabel("Cumulative Mass at T(CH$_3$OH) [M$_\\odot$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='upper left')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax2 = ax.twiny()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots * 3 + 9, figsize=figsize)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins * pixscale * 3600., tem_rprof, label=name)
pl.legend(loc='best')
pl.ylabel("CH$_3$OH temperature")
pl.xlabel("Radius [as]")
# Jeans length (radius = length/2)
azimuthal_average_RJ = (
c_s**1 /
(constants.G**0.5 *
(2.8 * u.Da * azimuthal_average_density)**0.5)).to(u.au) / 2.
pl.figure(nplots * 3 + 10, figsize=figsize)
pl.plot(angular_radii, azimuthal_average_RJ, label=name)
pl.plot([
0, (7000 * u.au / masscalc.distance).to(
u.arcsec, u.dimensionless_angles()).value
], [0, 7000],
'k--',
alpha=0.5)
pl.plot([
beam.major.to(u.arcsec).value,
(7000 * u.au / masscalc.distance).to(
u.arcsec, u.dimensionless_angles()).value
], [1000, 1000],
'k:',
alpha=0.5)
if ii == 0:
pl.fill_between([0, beam.major.to(u.arcsec).value], [8000, 8000],
zorder=-5,
alpha=0.1,
color='k')
pl.ylabel(
"Azimuthally Averaged $R_\\mathrm{J}$\nat $T(\\mathrm{CH}_3\\mathrm{OH})$ [au]"
)
pl.xlabel("Radius [arcsec]")
pl.ylim(0, 8000)
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots * 3 + 11, figsize=figsize)
line, = pl.plot((angular_radii[1:] + angular_radii[:-1]) / 2.,
density_alpha)
pl.plot((angular_radii[1:] + angular_radii[:-1]) / 2.,
density_alpha_,
'--',
color=line.get_color(),
alpha=0.5)
pl.xlabel("Radius [as]")
pl.ylabel("Density Profile Exponent $\\kappa_\\rho$")
pl.figure(nplots * 3 + 12, figsize=figsize)
pl.semilogy(angular_radii, azimuthal_average_density, label=name)
pl.ylabel("Azimuthally Averaged Density\nat T(CH$_3$OH) [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(1e6, 2e9)
ax.set_xlim(0, 1.2)
ax2 = ax.twiny()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 7000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
ax3 = ax.twinx()
def tick_function(old_x):
newx = ((constants.G * 2.8 * u.Da *
(old_x * u.cm**-3))**-0.5).to(u.kyr).value
return ["%.1f" % z for z in newx]
new_tick_locations = [1.3, 1.5, 2, 3, 5] * u.kyr
new_tick_locs_dens = ((new_tick_locations**2 * constants.G * 2.8 *
u.Da)**-1).to(u.cm**-3)
ax3.set_ylim(ax.get_ylim())
ax3.set_yticks(new_tick_locs_dens.value)
ax3.set_yticklabels(tick_function(new_tick_locs_dens.value))
ax3.set_ylabel(r"$t_{ff}$ [kyr]")
# start from 0.05 arcsec
xlim = u.Quantity([0.05, ax.get_xlim()[1]], u.arcsec)
xax_as = np.linspace(xlim[0], xlim[1], 100)
xax_m = (xax_as * masscalc.distance).to(u.m,
u.dimensionless_angles())
yax_s = ((ax.get_ylim() * u.cm**-3 * 2.8 * u.Da *
constants.G)**-0.5).to(u.s)
# plot 1 km/s line
line_1 = (((xax_m / (7.5 * u.km / u.s))**2 * constants.G * 2.8 *
u.Da)**-1).to(u.cm**-3)
line_2 = (((xax_m / (5 * u.km / u.s))**2 * constants.G * 2.8 *
u.Da)**-1).to(u.cm**-3)
line_3 = (((xax_m / (10 * u.km / u.s))**2 * constants.G * 2.8 *
u.Da)**-1).to(u.cm**-3)
line, = pl.plot(xax_as.value,
line_1.value,
'k:',
label='7.5 km s$^{-1}$',
zorder=-10)
labelLine(line, 0.45, "7.5 km s$^{-1}$")
line, = pl.plot(xax_as.value,
line_2.value,
'k:',
label='5 km s$^{-1}$',
zorder=-10)
labelLine(line, 0.6, "5 km s$^{-1}$")
line, = pl.plot(xax_as.value,
line_3.value,
'k-.',
label='10 km s$^{-1}$',
zorder=-10)
labelLine(line, 0.7, "10 km s$^{-1}$")
#ax.set_xlim(0,1.2)
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.figure(nplots * 3 + 13, figsize=figsize)
pl.plot(angular_radii, cumulative_profile_MJ, label=name)
pl.ylabel("$M_J(r<R)$ at T(CH$_3$OH) [$M_\odot$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii == len(names) - 1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(0, 2)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.figure(nplots * 3 + 14)
line, = pl.plot(angular_radii, azimuthal_average_MJ, label=name)
pl.plot(angular_radii,
azimuthal_average_mass,
linestyle='--',
color=line.get_color())
if ii == 0:
pl.fill_between([0, beam.major.to(u.arcsec).value], [100, 100],
[0, 0],
zorder=-5,
alpha=0.1,
color='k')
pl.ylabel("Mass at T(CH$_3$OH) [$M_\odot$]")
pl.xlabel("Radius [arcsec]")
handles, labels = ax.get_legend_handles_labels()
if '$\\bar{M}$' not in labels:
handles.append(
pl.matplotlib.lines.Line2D([], [], color='k', linestyle='--'))
labels.append('$\\bar{M}$')
if '$M_J$' not in labels:
handles.append(
pl.matplotlib.lines.Line2D([], [], color='k', linestyle='-'))
labels.append('$M_J$')
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(0, 5)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x * u.arcsec * masscalc.distance).to(
u.au, u.dimensionless_angles()).value
return ["%i" % z for z in newx]
new_tick_locations = np.arange(0, 8000, 1000) * u.au
new_tick_locs_as = (new_tick_locations / masscalc.distance).to(
u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 1.0, box.height])
ax2.set_position([box.x0, box.y0, box.width * 1.0, box.height])
# Put a legend to the right of the current axis
ax.legend(handles,
labels,
loc='center left',
bbox_to_anchor=(1, 0.5))
pl.figure(nplots * 3 + 15, figsize=figsize)
pl.subplot(2, 2, 1)
pl.semilogy(angular_radii, azimuthal_average_density, label=name)
pl.ylabel("Azimuthally Averaged Density\nat T(CH$_3$OH) [cm$^{-3}$]")
pl.subplot(2, 2, 3)
pl.semilogy(angular_radii, density_profile, label=name)
pl.ylabel("Cumulative Average Density\nat T(CH$_3$OH) [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
pl.subplot(2, 2, 2)
pl.plot(azimuthal_average_density, density_profile, label=name)
pl.plot(sorted(azimuthal_average_density.value),
sorted(azimuthal_average_density.value), 'k--')
pl.xlabel("Azimuthally Averaged Density\nat T(CH$_3$OH) [cm$^{-3}$]")
pl.ylabel("Cumulative Average Density\nat T(CH$_3$OH) [cm$^{-3}$]")
pl.subplot(2, 2, 4)
pl.ylabel("Azimuthal / Cumulative")
pl.plot(angular_radii,
azimuthal_average_density / density_profile,
label=name)
#pl.plot(angular_radii, [1.0] * len(angular_radii), 'k--')
pl.xlabel("Radius [arcsec]")
return locals()
def make_rprof(regions, ploteach=False):
names = [r.attr[1]['text'] for r in regions]
center_positions = coordinates.SkyCoord([r.coord_list
for r in regions],
unit=(u.deg, u.deg),
frame='fk5')
size = u.Quantity([1.25,1.25], u.arcsec)
if ploteach:
nplots = len(names)
for ii in range(nplots):
pl.figure(ii).clf()
pl.figure(nplots+ii).clf()
pl.figure(nplots*2+ii).clf()
linestyles = {name: itertools.cycle(['-'] + ['--'] + [':'] + ['-.'])
for name in names}
for fn in ffiles:
fh = fits.open(paths.dpath("12m/continuum/"+fn))
mywcs = wcs.WCS(fh[0].header)
if 'BMAJ' not in fh[0].header:
#print("File {0} does not have BMAJ".format(fn))
continue
try:
beam = radio_beam.Beam.from_fits_header(fh[0].header)
except KeyError:
#print("File {0} doesn't have beam info in the header".format(fn))
continue
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
ppbeam = (beam.sr/(pixscale**2*u.deg**2)).decompose().value / u.beam
#print("fn {0} ppbeam={1:0.2f}".format(fn, ppbeam))
for ii,(name,position) in enumerate(zip(names, center_positions)):
cutout = Cutout2D(fh[0].data, position, size, wcs=mywcs)
nr, bins, rprof = azimuthalAverage(cutout.data, binsize=1.0,
return_nr=True)
linestyle = next(linestyles[name])
pl.figure(ii)
pl.title(name)
pl.plot(bins*pixscale*3600., rprof/ppbeam,
label=fn.split(".")[0], linestyle=linestyle)
pl.ylabel("Azimuthally Averaged Flux (Jy)")
pl.xlabel("Radius [arcsec]")
cumul_rprof = np.nan_to_num(rprof*nr/ppbeam).cumsum()
pl.figure(nplots+ii)
pl.title(name)
pl.plot(bins*pixscale*3600., cumul_rprof,
label=fn.split(".")[0], linestyle=linestyle)
pl.ylabel("Cumulative Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if ii == 0:
ax = pl.gca()
ax2 = ax.twiny()
ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.pc, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = [0.005,0.01,0.015,0.02,0.025]*u.pc
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius (pc)")
ax3.set_ylim(ax.get_ylim())
yticks_mass = np.arange(0,6000,1000)
yticks_Jy = yticks_mass/masscalc.mass_conversion_factor().value
ax3.set_yticks(yticks_Jy)
ax3.set_yticklabels(yticks_mass)
ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=20$ K)")
pl.figure(nplots*2+ii)
pl.title(name)
pl.plot(((bins*pixscale*u.deg)*masscalc.distance).to(u.pc,
u.dimensionless_angles()),
cumul_rprof * masscalc.mass_conversion_factor(),
label=fn.split(".")[0], linestyle=linestyle)
pl.ylabel("Cumulative Mass (M$_\\odot$, $T=20$ K)")
pl.xlabel("Radius (pc)")
for ii in range(nplots):
for xtra in (0,nplots*2):
ax = pl.figure(ii+xtra).gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
else:
nplots = 0
pl.matplotlib.rc_file('pubfiguresrc')
for jj in range(nplots*3+1, nplots*3+15+1):
pl.figure(jj).clf()
# $ find ~/work/w51/alma/FITS/ -samefile ~/work/w51/alma/FITS/W51_te_continuum_best.fits
# /Users/adam/work/w51/alma/FITS//12m/continuum/selfcal_allspw_selfcal_3_mfs_deeper.image.pbcor.fits
# /Users/adam/work/w51/alma/FITS//W51_te_continuum_best.fits
fn = "selfcal_allspw_selfcal_3_mfs_deeper.image.pbcor.fits"
fh = fits.open(paths.dpath("12m/continuum/"+fn))
mywcs = wcs.WCS(fh[0].header)
beam = radio_beam.Beam.from_fits_header(fh[0].header)
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
ppbeam = (beam.sr/(pixscale**2*u.deg**2)).decompose().value / u.beam
for ii,(name,position) in enumerate(zip(names, center_positions)):
cutout = Cutout2D(fh[0].data, position, size, wcs=mywcs)
nr, bins, rprof = azimuthalAverage(cutout.data, binsize=1.0,
return_nr=True)
pl.figure(nplots*3+1)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., rprof/ppbeam,
label=name)
pl.ylabel("Azimuthally Averaged Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
cumul_rprof = np.nan_to_num(rprof*nr/ppbeam).cumsum()
pl.figure(nplots*3+2)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., cumul_rprof,
label=name)
pl.ylabel("Cumulative Flux (Jy)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax2 = ax.twiny()
ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
ax3.set_ylim(ax.get_ylim())
yticks_mass = np.arange(0,6000,1000)
yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
ax3.set_yticks(yticks_Jy)
ax3.set_yticklabels(yticks_mass)
ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
radii = ((bins*pixscale*u.deg)*masscalc.distance).to(u.pc,
u.dimensionless_angles())
mass_40k_profile = (cumul_rprof * masscalc.mass_conversion_factor(TK=40) / u.beam).to(u.M_sun)
pl.figure(nplots*3+3)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(radii, mass_40k_profile, label=name)
pl.ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.xlabel("Radius (pc)")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
density_40k_profile = (mass_40k_profile / (4/3.*np.pi*radii**3) / (2.8*u.Da)).to(u.cm**-3)
#print(density_40k_profile)
#print(radii)
pl.figure(nplots*3+4)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.semilogy(radii, density_40k_profile, label=name)
pl.ylabel("Cumulative Density [n(H$_2$), $T=40$ K]")
pl.xlabel("Radius (pc)")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
angular_radii = bins*pixscale*3600.
azimuthal_average_flux = rprof/ppbeam
azimuthal_average_mass_40K = azimuthal_average_flux * masscalc.mass_conversion_factor(TK=40) / u.beam
bindiff_as = np.diff(bins).mean() * pixscale * 3600.
bindiff_cm = bindiff_as * masscalc.distance / 206265.
bins_cm = (angular_radii * masscalc.distance / 206265.).to(u.cm)
sqdiffbins_cm = u.Quantity([bins_cm[0].to(u.cm).value**2] +
(bins_cm.to(u.cm)[1:]**2 -
bins_cm.to(u.cm)[:-1]**2).value.tolist(),
u.cm**2)
cudiffbins_cm = u.Quantity([bins_cm[0].to(u.cm).value**3] +
(bins_cm.to(u.cm)[1:]**3 -
bins_cm.to(u.cm)[:-1]**3).value.tolist(),
u.cm**3)
sqdiffbins_pix = [bins[0]**2] + (bins[1:]**2 - bins[:-1]**2).tolist()
azimuthal_average_density_40K = (azimuthal_average_mass_40K * sqdiffbins_pix /
(2.8*u.Da) /
(4/3.*np.pi*(cudiffbins_cm))).to(u.cm**-3)
pl.figure(nplots*3+5)
pl.semilogy(angular_radii, azimuthal_average_density_40K, label=name)
pl.ylabel("Azimuthally Averaged Density\nat T=40K [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
c_s_40k = ((constants.k_B * 40*u.K / (2.4*u.Da))**0.5).to(u.km/u.s)
azimuthal_average_MJ_40k = (np.pi/6. * c_s_40k**3 /
(constants.G**1.5 *
(2.8*u.Da*azimuthal_average_density_40K)**0.5)).to(u.M_sun)
pl.figure(nplots*3+6)
pl.semilogy(angular_radii, azimuthal_average_MJ_40k, label=name)
pl.ylabel("Azimuthally Averaged $M_J$\nat $T=40$K")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
source = 'e2' if name == 'e2e' else name
temperature_map_fn = paths.dpath('12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(source))
temperature_map_fh = fits.open(temperature_map_fn)
# this whole section is copied from overlay_contours_on_ch3oh
ch3ohN_hdul = fits.open(paths.dpath('12m/moments/CH3OH_{0}_cutout_columnmap.fits'.format(source)))
ch3ohT_hdul = fits.open(paths.dpath('12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(source)))
bigwcs = wcs.WCS(ch3ohT_hdul[0].header)
bigpixscale = (bigwcs.pixel_scale_matrix.diagonal()**2).sum()**0.5 * u.deg
ch3ohN = ch3ohN_hdul[0].data
ch3ohT = ch3ohT_hdul[0].data
dust_brightness,wts = reproject.reproject_interp(fits.open(paths.dpath('W51_te_continuum_best.fits')),
ch3ohN_hdul[0].header)
bm = radio_beam.Beam.from_fits_header(paths.dpath("W51_te_continuum_best.fits"))
yy,xx = np.indices(ch3ohN.shape)
if source == 'north':
center = [84.,38.]
else:
center = [ch3ohN.shape[0]/2., ch3ohN.shape[1]/2.]
yyc = (yy-center[0])
xxc = (xx-center[1])
rr = (yyc**2 + xxc**2)**0.5
rr_as = (rr*bigpixscale).to(u.arcsec)
theta = np.arctan2(yyc,xxc)*u.rad
dust_column = dust_emissivity.dust.colofsnu(225*u.GHz, dust_brightness*u.Jy,
beamomega=bm,
temperature=ch3ohT*u.K)
ch3oh_abundance = ch3ohN / dust_column.value
mask = (ch3oh_abundance > 1e-10) & (ch3oh_abundance < 1e-5)
if source == 'e2':
mask = mask & (((theta > 15*u.deg) & (theta < 345*u.deg)) | (theta < -15*u.deg))
mask = mask & np.isfinite(ch3oh_abundance)
# exclude high-abundance, low-column regions: likely to be div-by-zero zones
mask = mask & (~((ch3ohN < 1e18) & (ch3oh_abundance > 5e-6)))
mask = mask & (~((dust_brightness<1e-2) & (ch3ohT > 500) & (ch3oh_abundance > 1e-6)))
#mask = mask & (~((ch3ohT > 250) &
# (ch3ohN < 1e18) &
# (rr_as>1.5*u.arcsec))
# )# these are low-column,
temwcs = wcs.WCS(temperature_map_fh[0].header)
temcutout = Cutout2D(temperature_map_fh[0].data, position, size, wcs=temwcs)
maskcutout = Cutout2D(mask.astype('float'), position, size, wcs=bigwcs)
tem_nr, tem_bins, tem_rprof = azimuthalAverage(temcutout.data,
weights=(maskcutout.data>0).astype('float'),
binsize=1.0,
return_nr=True,
interpnan=True,
)
azimuthal_average_temperature = tem_rprof
mass_map = (cutout.data * masscalc.mass_conversion_factor(TK=temcutout.data) ).to(u.M_sun)
yy,xx = np.indices(cutout.data.shape)
rr_map = ((xx-cutout.data.shape[1]/2.)**2 + (yy-cutout.data.shape[0]/2.)**2)
mass_profile = (cumul_rprof *
masscalc.mass_conversion_factor(TK=tem_rprof) /
u.beam).to(u.M_sun)
density_profile = (mass_profile / (4/3.*np.pi*radii**3) /
(2.8*u.Da)).to(u.cm**-3)
azimuthal_average_mass = (azimuthal_average_flux *
masscalc.mass_conversion_factor(TK=tem_rprof)
/ u.beam)
# how accurate is this? We are measuring mass in cylindrical annuli,
# but we are assuming the underlying source structure is spherical
# See Cores.ipynb (in notes/, not in this repo) for a detailed
# analysis...
azimuthal_average_density = (azimuthal_average_mass * sqdiffbins_pix /
(2.8*u.Da) /
(4/3.*np.pi*(cudiffbins_cm))).to(u.cm**-3)
# this is not a good approximation for the spherial density profile...
# see cores.ipynb.
# Really should be this number +1 (so see the +1 below)
# Also, negatived...
density_alpha_ = 1-((np.log(density_profile[1:].value) -
np.log(density_profile[:-1].value)) /
(np.log(angular_radii[1:]) -
np.log(angular_radii[:-1]))
)
# from cores.ipynb, this actually gets (close to) the right answer
density_alpha = -((np.log(azimuthal_average_density[1:].value) -
np.log(azimuthal_average_density[:-1].value)) /
(np.log(angular_radii[1:]) -
np.log(angular_radii[:-1]))
)
c_s = ((constants.k_B * tem_rprof*u.K / (2.4*u.Da))**0.5).to(u.km/u.s)
azimuthal_average_MJ = (np.pi/6. * c_s**3 /
(constants.G**1.5 *
(2.8*u.Da*azimuthal_average_density)**0.5)
).to(u.M_sun)
cumulative_mass_weighted_temperature = ((azimuthal_average_mass *
azimuthal_average_temperature).cumsum()
/ mass_profile)
cumulative_c_s = ((constants.k_B *
cumulative_mass_weighted_temperature*u.K /
(2.4*u.Da))**0.5).to(u.km/u.s)
cumulative_profile_MJ = (np.pi/6. * cumulative_c_s**3 /
(constants.G**1.5 *
(2.8*u.Da*density_profile)**0.5)
).to(u.M_sun)
pl.figure(nplots*3+7)
line, = pl.plot(angular_radii, azimuthal_average_MJ, label=name)
# WRONG pl.plot(angular_radii, azimuthal_average_mass, linestyle='--',
# WRONG alpha=0.75, color=line.get_color())
#pl.plot(rr_map*pixscale*3600., mass_map, 'k.', alpha=0.25)
pl.axis([0,1.2,0.0,5.0])
pl.ylabel("Azimuthally Averaged $M_J$\nat $T(\\mathrm{CH}_3\\mathrm{OH})$ [$M_\\odot$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots*3+8)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., mass_profile,
label=name)
pl.ylabel("Cumulative Mass at T(CH$_3$OH)")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='upper left')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax2 = ax.twiny()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots*3+9)
#pl.title(fn.replace(".image.pbcor.fits",""))
pl.plot(bins*pixscale*3600., tem_rprof,
label=name)
pl.legend(loc='best')
pl.ylabel("CH$_3$OH temperature")
pl.xlabel("Radius [as]")
# Jeans length (radius = length/2)
azimuthal_average_RJ = (c_s**1 /
(constants.G**0.5 *
(2.8*u.Da*azimuthal_average_density)**0.5)
).to(u.au) / 2.
pl.figure(nplots*3+10)
pl.plot(angular_radii, azimuthal_average_RJ, label=name)
pl.plot([0,(7000*u.au/masscalc.distance).to(u.arcsec,
u.dimensionless_angles()).value],
[0,7000], 'k--', alpha=0.5)
pl.ylabel("Azimuthally Averaged $R_J$\nat $T(\\mathrm{CH}_3\\mathrm{OH})$ [au]")
pl.xlabel("Radius [arcsec]")
pl.ylim(0,8000)
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
#ax.set_ylim(1e7, 5e10)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
pl.figure(nplots*3+11)
line, = pl.plot((angular_radii[1:]+angular_radii[:-1])/2., density_alpha)
pl.plot((angular_radii[1:]+angular_radii[:-1])/2., density_alpha_, '--',
color=line.get_color(), alpha=0.5)
pl.xlabel("Radius [as]")
pl.ylabel("Density Profile Exponent $\\kappa_\\rho$")
pl.figure(nplots*3+12)
pl.semilogy(angular_radii, azimuthal_average_density, label=name)
pl.ylabel("Azimuthally Averaged Density\nat T=T(CH$_3$OH) [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(1e6, 2e9)
ax.set_xlim(0,1.2)
ax2 = ax.twiny()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,7000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
ax3 = ax.twinx()
def tick_function(old_x):
newx = ((constants.G * 2.8*u.Da * (old_x*u.cm**-3))**-0.5).to(u.kyr).value
return ["%.1f" % z for z in newx]
new_tick_locations = [1.3,1.5,2,3,5]*u.kyr
new_tick_locs_dens = ((new_tick_locations**2 * constants.G *
2.8*u.Da)**-1).to(u.cm**-3)
ax3.set_ylim(ax.get_ylim())
ax3.set_yticks(new_tick_locs_dens.value)
ax3.set_yticklabels(tick_function(new_tick_locs_dens.value))
ax3.set_ylabel(r"$t_{ff}$ [kyr]")
# start from 0.05 arcsec
xlim = u.Quantity([0.05, ax.get_xlim()[1]], u.arcsec)
xax_as = np.linspace(xlim[0], xlim[1], 100)
xax_m = (xax_as *
masscalc.distance).to(u.m, u.dimensionless_angles())
yax_s = ((ax.get_ylim() * u.cm**-3 * 2.8 * u.Da * constants.G)**-0.5).to(u.s)
# plot 1 km/s line
line_1 = (((xax_m / (7.5*u.km/u.s))**2 * constants.G *
2.8*u.Da)**-1).to(u.cm**-3)
line_2 = (((xax_m / (5*u.km/u.s))**2 * constants.G *
2.8*u.Da)**-1).to(u.cm**-3)
line_3 = (((xax_m / (10*u.km/u.s))**2 * constants.G *
2.8*u.Da)**-1).to(u.cm**-3)
line, = pl.plot(xax_as.value, line_1.value, 'k:',
label='7.5 km s$^{-1}$',
zorder=-10)
labelLine(line, 0.45, "7.5 km s$^{-1}$")
line, = pl.plot(xax_as.value, line_2.value, 'k:',
label='5 km s$^{-1}$',
zorder=-10)
labelLine(line, 0.6, "5 km s$^{-1}$")
line, = pl.plot(xax_as.value, line_3.value, 'k-.',
label='10 km s$^{-1}$',
zorder=-10)
labelLine(line, 0.7, "10 km s$^{-1}$")
#ax.set_xlim(0,1.2)
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.figure(nplots*3+13)
pl.plot(angular_radii, cumulative_profile_MJ, label=name)
pl.ylabel("$M_J(r<R)$ at T=T(CH$_3$OH) [$M_\odot$]")
pl.xlabel("Radius [arcsec]")
if len(names) < 5:
pl.legend(loc='best')
elif ii==len(names)-1:
ax = pl.gca()
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.6, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(0, 2)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
pl.figure(nplots*3+14)
line, = pl.plot(angular_radii, azimuthal_average_MJ, label=name)
pl.plot(angular_radii, azimuthal_average_mass,
linestyle='--', color=line.get_color())
pl.ylabel("Mass at T=T(CH$_3$OH) [$M_\odot$]")
pl.xlabel("Radius [arcsec]")
handles, labels = ax.get_legend_handles_labels()
if '$\\bar{M}$' not in labels:
handles.append(pl.matplotlib.lines.Line2D([],[],
color='k', linestyle='--'))
labels.append('$\\bar{M}$')
if '$M_J$' not in labels:
handles.append(pl.matplotlib.lines.Line2D([],[],
color='k', linestyle='-'))
labels.append('$M_J$')
if ii == len(names) - 1:
ax = pl.gca()
ax.set_ylim(0, 5)
ax2 = ax.twiny()
#ax3 = ax.twinx()
def tick_function(old_x):
newx = (old_x*u.arcsec*masscalc.distance).to(u.au, u.dimensionless_angles()).value
return ["%.1f" % z for z in newx]
new_tick_locations = np.arange(0,8000,1000)*u.au
new_tick_locs_as = (new_tick_locations/masscalc.distance).to(u.arcsec, u.dimensionless_angles())
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locs_as.value)
ax2.set_xticklabels(tick_function(new_tick_locs_as.value))
ax2.set_xlabel(r"Radius [au]")
#ax3.set_ylim(ax.get_ylim())
#yticks_mass = np.arange(0,6000,1000)
#yticks_Jy = yticks_mass/masscalc.mass_conversion_factor(TK=40).value
#ax3.set_yticks(yticks_Jy)
#ax3.set_yticklabels(yticks_mass)
#ax3.set_ylabel("Cumulative Mass (M$_\\odot$, $T=40$ K)")
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax2.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
ax.legend(handles, labels, loc='center left', bbox_to_anchor=(1, 0.5))
pl.figure(nplots*3+15)
pl.subplot(2,2,1)
pl.semilogy(angular_radii, azimuthal_average_density, label=name)
pl.ylabel("Azimuthally Averaged Density\nat T=T(CH$_3$OH) [cm$^{-3}$]")
pl.subplot(2,2,3)
pl.semilogy(angular_radii, density_profile, label=name)
pl.ylabel("Cumulative Average Density\nat T=T(CH$_3$OH) [cm$^{-3}$]")
pl.xlabel("Radius [arcsec]")
pl.subplot(2,2,2)
pl.plot(azimuthal_average_density, density_profile, label=name)
pl.plot(sorted(azimuthal_average_density.value),
sorted(azimuthal_average_density.value), 'k--')
pl.xlabel("Azimuthally Averaged Density\nat T=T(CH$_3$OH) [cm$^{-3}$]",
fontsize=12)
pl.ylabel("Cumulative Average Density\nat T=T(CH$_3$OH) [cm$^{-3}$]",
fontsize=12)
pl.subplot(2,2,4)
pl.ylabel("Azimuthal / Cumulative", fontsize=12)
pl.plot(angular_radii, azimuthal_average_density / density_profile,
label=name)
#pl.plot(angular_radii, [1.0] * len(angular_radii), 'k--')
pl.xlabel("Radius [arcsec]")
return locals()
luminosity_image70 = FITS_tools.hcongrid.hcongrid_hdu(fits.open(paths.dpath2('HIGAL_W51_mosaic_fit_070to500_luminosity.fits'))[0],
fits.getheader(h2co_densfile))
w = wcs.WCS(column_image.header)
dens_image = fits.open(h2co_densfile)[0]
w2 = wcs.WCS(dens_image.header)
pixsize = np.abs(w.wcs.get_cdelt()[0]) * u.deg
h2co_pixsize = np.abs(w2.wcs.get_cdelt()[0]) * u.deg
# Formaldehyde: mask out NaNs
x,y = w2.wcs_world2pix([center.l.deg], [center.b.deg], 0)
data = dens_image.data
mask = np.isfinite(data)
data[~mask] = 0
result = radialprofile.azimuthalAverage(data, center=(x,y), return_nr=True, mask=mask, interpnan=True)
h2co_nr, h2co_centers, h2co_rprof = result
h2co_ok = np.isfinite(h2co_rprof)
#h2co_nr = h2co_nr[h2co_ok]
#h2co_centers = h2co_centers[h2co_ok]
#h2co_rprof = h2co_rprof[h2co_ok]
h2co_mindensfile = paths.dpath("H2CO_ParameterFits_min_mean_density_stdmasked.fits")
mindens_image = fits.open(h2co_mindensfile)[0]
mindata = np.nan_to_num(mindens_image.data)
result = radialprofile.azimuthalAverage(mindata, center=(x,y), return_nr=True, mask=mask, interpnan=True)
minh2co_nr, minh2co_centers, minh2co_rprof = result
x,y = w.wcs_world2pix([center.l.deg], [center.b.deg], 0)
