def plotit():
import astropy.stats
import pylab as pl
pl.figure(1).clf()
pl.figure(2).clf()
for ii,imname in enumerate(files):
print(imname)
data = files[imname]['file'].data
brickdata = brick_files[imname]['file'].data
# https://github.com/astropy/astropy/pull/5232 for ignore_nan
lo = astropy.stats.mad_std(data, ignore_nan=True)
hi = np.nanmax(data)
bins = np.logspace(np.log10(lo), np.log10(hi), 100)
pl.figure(1)
pl.subplot(3,3,ii+1)
brickweights = np.ones(np.isfinite(brickdata).sum(), dtype='float')/np.isfinite(brickdata).sum()*np.isfinite(data).sum()
bH,bL,bP = pl.hist(brickdata[np.isfinite(brickdata)], bins=bins,
log=True, alpha=0.5, histtype='step',
#weights=brickweights,
bottom=1e-100,
color='b', zorder=-1)
#weights = np.ones(np.isfinite(data).sum(), dtype='float')/np.isfinite(data).sum()
H,L,P = pl.hist(data[np.isfinite(data)], bins=bins, log=True,
alpha=0.5, color='k',
#normed=True,
histtype='step')
#pl.hist(tbl[imname], bins=bins, log=True, alpha=0.5)
pl.xlim(np.min([bL.min(), L.min()]), L.max())
pl.semilogx()
pl.yscale('log', nonposy='clip')
ax2 = pl.gca().twinx()
ax2.plot(sorted(tbl[imname]), np.arange(len(tbl),
dtype='float')/len(tbl),
'k-', linewidth=3, alpha=0.5, zorder=10)
ax2.set_xlim(L.min(), L.max())
ax2.set_ylim(0,1)
pl.title(imname)
pl.savefig(paths.fpath("flux_histograms_with_core_location_CDF.png"))
pl.figure(2)
pl.subplot(3,3,ii+1)
pl.imshow(data, cmap='gray_r')
pl.contour(data, levels=np.percentile(tbl[imname],[5,10,25,50,75,90,95]))
pl.title(imname)
def main():
make_model()
densities, dgrid_exp, dgrid_ML, dngrid_exp, dngrid_ML = diffplot_t_of_n()
pl.figure(1).savefig(paths.fpath('param_fits/modeled_temperature_vs_density_real_vs_recovered_C=13.5.png'), bbox_inches='tight')
pl.figure(2).savefig(paths.fpath('param_fits/modeled_temperature_vs_density_real_vs_recovered_fractions_C=13.5.png'), bbox_inches='tight')
pl.figure(3).savefig(paths.fpath('param_fits/modeled_density_vs_density_real_vs_recovered_C=13.5.png'), bbox_inches='tight')
pl.figure(4).savefig(paths.fpath('param_fits/modeled_density_vs_density_real_vs_recovered_fractions_C=13.5.png'), bbox_inches='tight')
columns, cgrid_exp, cgrid_ML = diffplot_t_of_c()
pl.figure(1).savefig(paths.fpath('param_fits/modeled_temperature_vs_column_real_vs_recovered_n=4.5.png'), bbox_inches='tight')
pl.figure(2).savefig(paths.fpath('param_fits/modeled_temperature_vs_column_real_vs_recovered_fractions_n=4.5.png'), bbox_inches='tight')
return locals()
def make_model_plot(**kwargs):
constraints,mf,density,column,temperature = make_model(**kwargs)
pl.figure(1)
mf.parplot1d('dens')
pl.vlines(density, 0, pl.gca().get_ylim()[1], color='k', zorder=-10)
pl.figure(2)
mf.parplot1d('tem')
pl.vlines(temperature, 0, pl.gca().get_ylim()[1], color='k', zorder=-10)
pl.figure(3)
mf.parplot1d('col')
pl.vlines(column, 0, pl.gca().get_ylim()[1], color='k', zorder=-10)
pl.figure(4)
mf.parplot1d_all(levels=[0.68268949213708585])
pl.subplot(3,1,1).vlines(column, 0, pl.gca().get_ylim()[1], color='k', zorder=-10)
pl.subplot(3,1,2).vlines(density, 0, pl.gca().get_ylim()[1], color='k', zorder=-10)
pl.subplot(3,1,3).vlines(temperature, 0, pl.gca().get_ylim()[1], color='k', zorder=-10)
pl.suptitle('t={0:0.1f}_c={1:0.1f}_n={2:0.1f}.png'.format(temperature,
column,
density,))
pl.savefig(paths.fpath('param_fits/oned_fit_parameters_example_t={0:0.1f}_c={1:0.1f}_n={2:0.1f}.png'.format(temperature,
column,
density,)),
bbox_inches='tight')
pl.figure(5)
mf.denstemplot()
pl.plot(density, temperature, 'o', markerfacecolor='none', markeredgecolor='r', markeredgewidth=2)
pl.figure(7)
mf.denscolplot()
pl.plot(density, column, 'o', markerfacecolor='none', markeredgecolor='r', markeredgewidth=2)
pl.figure(8)
mf.coltemplot()
pl.plot(column, temperature, 'o', markerfacecolor='none', markeredgecolor='r', markeredgewidth=2)
pl.draw()
pl.show()
return constraints,mf
def fit_all_objects(dend=dend, **kwargs):
fits = {}
for obj in dend:
if obj.bad:
log.info("Skipping object {0} because it is HC3N or other.".format(obj.idx))
continue
log.info("Operating on object {0}".format(obj.idx))
try:
sp = fit_object(obj, plot=False, **kwargs)
except ValueError as ex:
log.info("Exception for {0}: {1}".format(obj.idx, ex.message))
continue
if sp is None:
log.info("No result for {0}".format(obj.idx))
continue
sp.plotter(figure=pl.figure(1))
sp.specfit.plot_fit(show_components=True)
sp.plotter.savefig(fpath('dendro/dendro_fit_obj{0:04d}.png'.format(obj.idx)))
fits[obj.idx] = sp.specfit.parinfo
return fits
min_cut=vmin*1e3,
max_cut=vmax*1e3,
asinh_a=0.001),
transform=ax.get_transform(cutout_res.wcs),
origin='lower',)
#(x1,y1),(x2,y2) = mywcs.wcs_world2pix([[ra1,dec1]],0)[0], mywcs.wcs_world2pix([[ra2,dec2]],0)[0]
(x1,y1),(x2,y2) = cutout_im.wcs.wcs_world2pix([[ra1,dec1]],0)[0], cutout_im.wcs.wcs_world2pix([[ra2,dec2]],0)[0]
ax.axis([x1,x2,y1,y2])
ax2.axis([x1,x2,y1,y2])
#tr_fk5 = ax.get_transform("fk5")
hide_labels(ax)
hide_labels(ax2)
#ax.plot([ra1,ra2], [dec1,dec2], transform=tr_fk5, marker='o', color='r', zorder=50)
fig.subplots_adjust(wspace=0, hspace=0)
fig.canvas.draw()
x1 = ax.bbox.x1 / (fig.bbox.x1-fig.bbox.x0)
# y0 = ax.bbox.y0 / (fig.bbox.y1-fig.bbox.y0)
y0 = ax2.bbox.y0 / (fig.bbox.y1-fig.bbox.y0)
# single-ax version height = (ax.bbox.y1-ax.bbox.y0) / (fig.bbox.y1-fig.bbox.y0)
height = (ax.bbox.y1-ax2.bbox.y0) / (fig.bbox.y1-fig.bbox.y0)
cax_bbox = [x1 + 0.02, y0, 0.02, height]
cb = pl.colorbar(mappable=im, cax=fig.add_axes(cax_bbox))
cb.set_label('mJy/beam')
fig.savefig(paths.fpath("selfcal_progression_TCTE7m_try2_{0}.pdf".format(name)),
bbox_inches='tight', dpi=300)
def fourier_suppression(e1,
hdr,
withline=False,
vrange=[100, 'max'],
linewidth=20.,
linecen=250.,
save=False,
suppression_factor=3,
amplitude=10,
interpolate=False,
convinterp=True,
smooth_width=2):
"""
Given a spectrum (fn), suppress a velocity range `vrange` by dividing it by
some factor. Then plot...
"""
vres = hdr['CDELT1']
ff = np.fft.fftfreq(e1.size)
fvr = e1.size * vres
fvelo = fvr / (ff / ff[1]) / 2.
fvelo[0] = fvr
velo = vres * (np.arange(e1.size) + 1 - hdr['CRPIX1']) + hdr['CRVAL1']
# Add a 20 km/s wide Gaussian line, see what happens to it
line = np.exp(-(velo + linecen)**2 / (linewidth**2 * 2.)) * amplitude
e2 = line + e1
ft = np.fft.fft(e1)
ft2 = np.fft.fft(e2)
#ft[(abs(fvelo)>100) & (abs(fvelo<165))] /= abs(ft[(abs(fvelo)>100) & (abs(fvelo<165))])/1000
#ft[(abs(fvelo)>230) & (abs(fvelo<556))] /= abs(ft[(abs(fvelo)>230) & (abs(fvelo<556))])/1000
if 'max' in vrange:
vrange[1] = max(fvelo) + 1
offset = 0
else:
offset = 10
lmask = (abs(fvelo) < vrange[1])
umask = (abs(fvelo) > vrange[0])
mask = lmask & umask
levelmask = (abs(fvelo) < vrange[0])
if interpolate:
midpt = len(mask) / 2
whmask, = np.where(mask)
startpt = whmask[whmask < midpt][0] - 1
endpt = whmask[whmask < midpt][-1] + 1
if endpt >= len(mask):
endpt = len(mask) - 1
for fff in (ft, ft2):
if convinterp:
mdata = fff.copy()
mdata[mask] = np.nan
#realdata = convolve(mdata.real, Gaussian1DKernel(1, x_size=51), boundary='extend')
#imagdata = convolve(mdata.imag, Gaussian1DKernel(1, x_size=51), boundary='extend')
#interpdata = (realdata[mask]+1j*imagdata[mask])
amp = convolve(np.abs(mdata),
Gaussian1DKernel(smooth_width, x_size=51),
boundary='extend')
phase = convolve(np.angle(mdata),
Gaussian1DKernel(smooth_width, x_size=51),
boundary='extend')
interpdata = (np.cos(phase) * amp +
1j * np.sin(phase) * amp)[mask]
#pl.figure(5)
#pl.clf()
#ax1 = pl.subplot(3,1,1)
#ax1.plot(np.arange(1,30), fff.real[1:30])
#ax1.plot(np.arange(1,30), mdata.real[1:30])
#ax1.plot(whmask[:len(whmask)/2], interpdata.real[:len(whmask)/2],'--')
#ax2 = pl.subplot(3,1,2)
#ax2.plot(np.arange(1,30), fff.imag[1:30])
#ax2.plot(np.arange(1,30), mdata.imag[1:30])
#ax2.plot(whmask[:len(whmask)/2], interpdata.imag[:len(whmask)/2],'--')
#ax3 = pl.subplot(3,1,3)
#ax3.plot(np.arange(1,30), abs(fff)[1:30])
#ax3.plot(np.arange(1,30), abs(mdata)[1:30])
#ax3.plot(whmask[:len(whmask)/2], abs(interpdata[:len(whmask)/2]),'--')
fff[mask] = interpdata
#ax1.plot(np.arange(1,30), fff.real[1:30],':')
#ax2.plot(np.arange(1,30), fff.imag[1:30],':')
#ax3.plot(np.arange(1,30), abs(fff)[1:30],':')
else:
for order, compare in zip((-1, 1),
(np.less, np.greater_equal)):
mm = mask & compare(np.arange(len(mask), dtype='int'),
midpt)
realdata = np.interp(np.arange(startpt + 1,
endpt), [startpt, endpt],
[fff.real[startpt], fff.real[endpt]])
imagdata = np.interp(np.arange(startpt + 1,
endpt), [startpt, endpt],
[fff.imag[startpt], fff.imag[endpt]])
fff[mm] = (realdata + 1j * imagdata)[::order]
else:
level = max(abs(ft)[levelmask]) / suppression_factor
level2 = max(abs(ft2)[levelmask]) / suppression_factor
ft[(mask)] /= abs(ft[(mask)]) / level
ft2[(mask)] /= abs(ft2[(mask)]) / level2
ax1 = pl.figure(1).gca()
ax1.cla()
ax1.plot(velo, e1, linestyle='none', color='k', marker=',', label='Input')
ax1.plot(velo,
np.fft.ifft(ft) + offset,
alpha=0.8,
linestyle='none',
marker=',',
color='r',
label='FT suppressed')
ax1.set_xlabel("Velocity")
ax1.set_ylabel("Brightness (K-ish)")
leg = ax1.legend(loc='best', markerscale=20)
for ll in leg.get_lines():
ll.set_marker('o')
ll.set_markersize(5)
if save:
pl.figure(1).savefig(
paths.fpath('baselines/ft_suppression_spectra.png'))
ax4 = pl.figure(4).gca()
ax4.cla()
ax4.set_title("Synthetic {0} km/s line".format(linewidth))
ax4.plot(velo,
e2 - np.median(e2),
linestyle='none',
color='b',
marker=',',
zorder=-1,
alpha=0.5,
label='Input')
ax4.plot(velo,
np.fft.ifft(ft2) + offset - np.median(e2),
alpha=0.5,
linestyle='none',
marker=',',
color='g',
zorder=-1,
label='FT Suppressed')
ax4.plot(velo,
e2 - np.fft.ifft(ft2) - offset,
alpha=0.5,
linestyle='none',
marker=',',
color='r',
zorder=-1,
label='(a) Diff')
ax4.plot(velo,
e1 - np.fft.ifft(ft) - offset * 2,
alpha=0.5,
linestyle='none',
marker=',',
color='m',
zorder=-1,
label='(b) Diff with no synthetic line')
ax4.plot(velo,
(e2 - np.fft.ifft(ft2)) - (e1 - np.fft.ifft(ft)) - offset * 3,
alpha=0.5,
linestyle='none',
marker=',',
color='k',
zorder=-1,
label='(a)-(b)')
ax4.plot(velo,
line + offset,
alpha=0.5,
linewidth=0.5,
color='k',
zorder=1,
label='Synthetic Line')
ax4.set_xlabel("Velocity")
ax4.set_ylabel("Brightness (K-ish)")
leg = ax4.legend(loc='lower left', markerscale=20, fontsize=16)
for ll in leg.get_lines():
ll.set_marker('o')
ll.set_markersize(5)
if save:
pl.figure(4).savefig(
paths.fpath('baselines/ft_suppression_spectra_synthline.png'))
ax4.axis([1300, 1700, -35, 21])
pl.figure(4).savefig(
paths.fpath('baselines/ft_suppression_spectra_synthline_zoom.png'))
ax2 = pl.figure(2).gca()
ax2.cla()
ax2.loglog(fvelo, abs(np.fft.fft(e1)), linewidth=1, label='Input')
ax2.loglog(fvelo,
abs(np.fft.fft(e2)),
linewidth=1,
label='Synthetic {0} km/s line'.format(linewidth))
ax2.loglog(fvelo, abs(ft), linewidth=1, label='FT Suppressed (Input)')
ax2.loglog(fvelo, abs(ft2), linewidth=1, label='FT Suppressed (Synthetic)')
ax2.set_xlabel("Velocity Scale")
ax2.set_ylabel("$|FT(spectrum)|$")
ax2.legend(loc='best')
if save:
pl.figure(2).savefig(
paths.fpath('baselines/ft_suppression_fourierpower.png'))
ax3 = pl.figure(3).gca()
ax3.cla()
ax3.plot(velo, e1 - np.fft.ifft(ft), label='Original')
ax3.plot(velo,
e2 - np.fft.ifft(ft2),
label='Synthetic {0} km/s line'.format(linewidth))
ax3.set_xlabel("Velocity")
ax3.set_ylabel("Difference (Input-FT_suppressed)")
leg = ax3.legend(loc='best', markerscale=20)
if save:
pl.figure(3).savefig(
paths.fpath('baselines/ft_suppression_differences.png'))
pl.draw()
pl.show()
# FFB6.remove_layer(lyr)
for ii,ind in enumerate(whmask):
ind = int(ind)
vel = downsampled_contsub_rrl30a.spectral_axis[ind]
slc = downsampled_contsub_rrl30a[ind,:,:]
color = pl.cm.Spectral((ind-whmask.min())/mask.sum())
FFB6.show_contour(slc.hdu, levels=[slc.max().value*0.95], colors=[color])
FFB6.ax.annotate("{0:0.1f}".format(vel),
(0.20, 0.88-0.02*ii),
xycoords='figure fraction',
color=color)
#FFB6.ax.axis((41.559800470110005, 76.83040770863137, 38.045746948239589, 63.824569975782737))
FFB6.recenter(83.80877507, -5.372957363, radius=(0.10*u.arcsec).to(u.deg).value)
FFB6.save(paths.fpath('rrls/BN_H30a_velocity_contours_on_BNcontinuum.pdf'), dpi=150)
FFB6.save(paths.fpath('rrls/BN_H30a_velocity_contours_on_BNcontinuum.png'))
mask = ((downsampled_contsub_rrl40a.spectral_axis > -10*u.km/u.s) & (downsampled_contsub_rrl40a.spectral_axis < 50*u.km/u.s))
whmask = np.where(mask)[0]
cont_B3 = spectral_cube.lower_dimensional_structures.Projection.from_hdu(fits.open(paths.dpath('Orion_SourceI_B3_continuum_r-2.clean0.1mJy.image.tt0.pbcor.fits')))
BNcutout_cont_B3 = cont_B3[5569:5627,5333:5397]
BNcutout_cont_B3.quicklook()
def spectral_overlays(fn, name, freq_name_mapping, frequencies, yoffset,
minvelo, maxvelo, suffix="", background_fn=None,
return_spectra=False, plot_fullspec=True):
object_data_dict = {}
spectra = pyspeckit.Spectra([fn.format(name=name, ii=ii)
for ii in range(4)])
bad_1 = (233.74*u.GHz<spectra.xarr.to(u.GHz)) & (spectra.xarr.to(u.GHz)<234.036*u.GHz)
bad_2 = (230.00*u.GHz<spectra.xarr.to(u.GHz)) & (spectra.xarr.to(u.GHz)<230.523*u.GHz)
bad_3 = (spectra.xarr.to(u.GHz) < 218.11*u.GHz)
spectra.data[bad_1 | bad_2 | bad_3] = np.nan
beams = [radio_beam.Beam.from_fits_header(sp.header) for sp in spectra]
# scaling: determine how much to separate spectra by vertically
scaling = np.nanmax(spectra.data) - np.nanpercentile(spectra.data, 20)
print("Scaling for {fn} = {scaling}".format(fn=fn.format(name=name, ii=0),
scaling=scaling))
if np.isnan(scaling):
raise ValueError("All-nan slice encountered. There is apparently no data in this file?")
if background_fn is not None:
bgspectra = pyspeckit.Spectra([background_fn.format(name=name, ii=ii)
for ii in range(4)])
bgspectra.data[bad_1 | bad_2 | bad_3] = np.nan
bg = True
else:
bg = False
fig = pl.figure(0)
fig.clf()
for spwnum,sp in enumerate(spectra):
bad_1 = (233.74*u.GHz<sp.xarr.to(u.GHz)) & (sp.xarr.to(u.GHz)<234.036*u.GHz)
bad_2 = (230.00*u.GHz<sp.xarr.to(u.GHz)) & (sp.xarr.to(u.GHz)<230.523*u.GHz)
bad_3 = (sp.xarr.to(u.GHz) < 218.11*u.GHz)
sp.data[bad_1 | bad_2 | bad_3] = np.nan
# temporary hack for bad data
if bg and all(bgspectra[spwnum].data == 0):
bg = False
(cont, peak, peakfreq, peakfreq_shifted, bestmatch, peakvelo, velo_OK,
peakspecies, argmax) = quick_analyze(sp, freq_name_mapping, minvelo, maxvelo)
object_data_dict['continuum20pct{0}'.format(spwnum)] = cont
object_data_dict['peak{0}freq'.format(spwnum)] = peakfreq
object_data_dict['peak{0}velo'.format(spwnum)] = peakvelo if velo_OK else np.nan*u.km/u.s
object_data_dict['peak{0}species'.format(spwnum)] = peakspecies
object_data_dict['peak{0}'.format(spwnum)] = (peak if velo_OK else np.nan)*u.Jy/u.beam
object_data_dict['beam{0}area'.format(spwnum)] = beams[spwnum].sr.value
if bg:
(bgcont, bgpeak, bgpeakfreq, bgpeakfreq_shifted, bgbestmatch,
bgpeakvelo, bgvelo_OK, bgpeakspecies,
bgargmax) = quick_analyze(bgspectra[spwnum], freq_name_mapping,
minvelo, maxvelo)
object_data_dict['bgpeak{0}freq'.format(spwnum)] = bgpeakfreq
object_data_dict['bgpeak{0}velo'.format(spwnum)] = bgpeakvelo if bgvelo_OK else np.nan*u.km/u.s
object_data_dict['bgpeak{0}species'.format(spwnum)] = bgpeakspecies
object_data_dict['bgpeak{0}'.format(spwnum)] = (bgpeak if bgvelo_OK else np.nan)*u.Jy/u.beam
log.debug("spw{0} peak{0}={1} line={2}".format(spwnum,
peak,
peakspecies)
)
for linename, freq in frequencies.items():
if sp.xarr.in_range(freq):
print("Plotting {0} for {1} from spw {2}. Peakspecies={3}".format(linename, name, spwnum, peakspecies))
temp = np.array(sp.xarr)
sp.xarr.convert_to_unit(u.km/u.s, refX=freq)
if np.isnan(yoffset[linename]) or np.isnan(scaling):
raise ValueError("NAN scaling is stupid.")
if sp.slice(0*u.km/u.s, 120*u.km/u.s).data.max() > sp.slice(0*u.km/u.s, 120*u.km/u.s).data.min():
sp.plotter(figure=fig, clear=False, offset=yoffset[linename]*scaling,
xmin=0, xmax=120)
sp.plotter.axis.text(122, yoffset[linename]*scaling,
"{0}: {1}".format(spwnum, linename))
sp.xarr.convert_to_unit(u.GHz, refX=freq)
np.testing.assert_allclose(temp, np.array(sp.xarr))
if bg:
bgs = bgspectra[spwnum]
bgs.xarr.convert_to_unit(u.km/u.s, refX=freq)
if bgs.slice(0*u.km/u.s, 120*u.km/u.s).data.max() > bgs.slice(0*u.km/u.s, 120*u.km/u.s).data.min():
bgs.plotter(axis=sp.plotter.axis,
clear=False,
color='b',
offset=yoffset[linename]*scaling,
zorder=-100,
xmin=0,
xmax=120,
)
bgs.xarr.convert_to_unit(u.GHz, refX=freq)
if sp.plotter.axis is not None:
sp.plotter.axis.set_ylim(-0.1, max(yoffset.values())*scaling+scaling)
if linename == peakspecies:
print('peakvelo, offset, maxdata: ', peakvelo,
yoffset[linename]*scaling, sp.data[argmax])
sp.plotter.axis.plot(peakvelo, yoffset[linename]*scaling +
sp.data[argmax], 'rx')
okvelos = [object_data_dict['peak{0}velo'.format(ii)] for ii in range(4) if
(minvelo < object_data_dict['peak{0}velo'.format(ii)]) and
(object_data_dict['peak{0}velo'.format(ii)] < maxvelo)]
if okvelos:
velo = np.median(u.Quantity(okvelos))
object_data_dict['mean_velo'] = velo
sp.plotter.axis.vlines(velo.to(u.km/u.s).value,
sp.plotter.axis.get_ylim()[0],
sp.plotter.axis.get_ylim()[1],
linestyle='--', linewidth=2, color='r',
zorder=-50, alpha=0.5)
else:
object_data_dict['mean_velo'] = np.nan*u.km/u.s
velo = 60*u.km/u.s
fig.savefig(paths.fpath("spectral_overlays/{name}_overlaid_spectra{suffix}.png".format(name=name, suffix=suffix)),
bbox_inches='tight', bbox_extra_artists=[])
# plot type #2: full spectrum, with lines ID'd
if plot_fullspec:
linenames = list(frequencies.keys())
freqs_ghz = list(frequencies.values())
plot_kwargs = {'color':'r', 'linestyle':'--'}
annotate_kwargs = {'color': 'r'}
for spwnum,sp in enumerate(spectra):
fig = pl.figure(spwnum+1)
fig.clf()
sp.xarr.convert_to_unit(u.GHz)
sp.plotter(figure=fig, axis=fig.gca())
# labels don't update. still don't know why
sp.plotter(figure=fig, axis=fig.gca())
assert sp.plotter._xunit == u.GHz
assert sp.plotter.xlabel == 'Frequency (GHz)'
sp.plotter.line_ids(linenames,
u.Quantity(freqs_ghz),
velocity_offset=velo,
plot_kwargs=plot_kwargs,
annotate_kwargs=annotate_kwargs)
assert sp.plotter.xlabel == 'Frequency (GHz)'
if bg:
bgs = bgspectra[spwnum]
bgs.xarr.convert_to_unit(u.GHz)
bgs.plotter(axis=sp.plotter.axis,
clear=False,
color='b',
zorder=-100,
)
assert bgs.plotter.xlabel == 'Frequency (GHz)'
assert sp.plotter.xlabel == 'Frequency (GHz)'
fig.savefig(paths.fpath("spectral_overlays/{name}_spw{spw}_fullspec{suffix}.png".format(name=name,
spw=spwnum,
suffix=suffix)),
bbox_inches='tight', bbox_extra_artists=[])
if return_spectra:
return object_data_dict, spectra
else:
return object_data_dict
def plot_whole_spectrum(spectra, line_id=line_ids, velocity=55*u.km/u.s,
fignum=1, figsize=(8.3,11.7), figname=None,
title=None, do_line_ids=True,
fontsize=4, linewidth=0.25):
pl.close(fignum)
fig = pl.figure(fignum, figsize=figsize)
with mpl.rc_context(rc={'font.size': 12,
'axes.labelsize':'medium',
'axes.titlesize':'medium'}):
ax = fig.add_subplot(7,1,1)
spectra[0].xarr.convert_to_unit(u.GHz)
spectra[0].xarr.convert_to_unit(u.GHz)
spectra[0].plotter(axis=ax)
assert spectra[0].plotter.xlabel == 'Frequency (GHz)'
if do_line_ids:
spectra[0].plotter.line_ids(list(line_id.keys()),
list(line_id.values()),
velocity_offset=velocity,
label1_size=fontsize,
plot_kwargs={'linewidth':linewidth},
max_iter=10,
)
for ii in (1,2,3):
ax = fig.add_subplot(7,1,2*ii)
spectra[ii].xarr.convert_to_unit(u.GHz)
cropspec = spectra[ii][:spectra[ii].shape[0]/2]
cropspec.plotter(axis=ax)
if do_line_ids:
cropspec.plotter.line_ids(list(line_id.keys()),
list(line_id.values()),
velocity_offset=velocity,
label1_size=fontsize,
plot_kwargs={'linewidth':linewidth},
#annotate_kwargs={'fontsize':fontsize},
max_iter=10,
)
ax = fig.add_subplot(7,1,2*ii+1)
cropspec = spectra[ii][spectra[ii].shape[0]/2:]
cropspec.plotter(axis=ax)
if do_line_ids:
cropspec.plotter.line_ids(list(line_id.keys()),
list(line_id.values()),
velocity_offset=velocity,
label1_size=fontsize,
plot_kwargs={'linewidth':linewidth},
max_iter=10,
#annotate_kwargs={'fontsize':fontsize},
)
for ii in range(1,8):
ax = pl.subplot(7,1,ii)
ax.set_ylabel("$T_B$ [K]")
if title is not None:
pl.subplot(7,1,1).set_title(title)
if figname is not None:
pl.savefig(paths.fpath(figname), dpi=300, bbox_inches='tight',
bbox_extra_artists=[])
F.tick_labels.set_yformat('dd.dd')
F.tick_labels.set_font(size=20)
F.axis_labels.set_font(size=20)
F.show_grayscale(stretch='arcsinh',vmin=-5e-4,vmax=0.011)
#e1 = coordinates.ICRS(290.93263,14.50745,unit=('deg','deg'))
#F.recenter(e1.ra.value,e1.dec.value,width=1/60.,height=1/60.)
#F.recenter(290.92633,14.514769,radius=1.4/60.)
F.recenter(290.92345,14.511772,radius=1.5/60.)
F.add_scalebar(length=((0.5 * u.pc)/distance*u.radian).to(u.degree).value)
F.scalebar.set_label('0.5 pc')
F.scalebar.set_color('orange')
F.scalebar.set_linewidth(3)
F.scalebar.set_font_size(20)
F.save(fpath('W51_C_grayscale.pdf'))
F.show_regions(rpath('HCHII_candidates.reg'))
#F.show_regions('/Users/adam/work/w51/cycle2_ALMA_frame2.reg')
F.save(fpath('W51_C_grayscale_HCHIIcandidates.pdf'))
F.scalebar.set_length(((0.1 * u.pc)/distance*u.radian).to(u.degree).value)
F.scalebar.set_label('0.1 pc')
F.recenter(290.91644,14.518939,radius=0.3/60.)
cube = SpectralCube.read(dpath(cube_names['11_uniform'])).with_spectral_unit(u.km/u.s, velocity_convention='radio')
for velo in np.arange(60,72,0.5):
c = pl.cm.jet_r((70-velo)/10.)
colors = [c[:3] + (x,) for x in (0.9,0.7,0.5,0.3,0.1)]
def measure_dendrogram_properties(dend=None, cube303=cube303,
cube321=cube321, cube13co=cube13co,
cube18co=cube18co, noise_cube=noise_cube,
sncube=sncube,
suffix="",
last_index=None,
plot_some=True,
line='303',
write=True):
assert (cube321.shape == cube303.shape == noise_cube.shape ==
cube13co.shape == cube18co.shape == sncube.shape)
assert sncube.wcs is cube303.wcs is sncube.mask._wcs
metadata = {}
metadata['data_unit'] = u.K
metadata['spatial_scale'] = 7.2 * u.arcsec
metadata['beam_major'] = 30 * u.arcsec
metadata['beam_minor'] = 30 * u.arcsec
metadata['wavelength'] = 218.22219*u.GHz
metadata['velocity_scale'] = u.km/u.s
metadata['wcs'] = cube303.wcs
keys = [
'density_chi2',
'expected_density',
'dmin1sig_chi2',
'dmax1sig_chi2',
'column_chi2',
'expected_column',
'cmin1sig_chi2',
'cmax1sig_chi2',
'temperature_chi2',
'expected_temperature',
'tmin1sig_chi2',
'tmax1sig_chi2',
'eratio321303',
'ratio321303',
'logh2column',
'elogh2column',
'logabundance',
'elogabundance',
]
obs_keys = [
'Stot303',
'Smin303',
'Smax303',
'Stot321',
'Smean303',
'Smean321',
'npix',
'e303',
'e321',
'r321303',
'er321303',
'13cosum',
'c18osum',
'13comean',
'c18omean',
's_ntotal',
'index',
'is_leaf',
'parent',
'root',
'lon',
'lat',
'vcen',
'higaldusttem',
'reff',
'dustmass',
'dustmindens',
'bad',
#'tkin_turb',
]
columns = {k:[] for k in (keys+obs_keys)}
log.debug("Initializing dendrogram temperature fitting loop")
# FORCE wcs to match
# (technically should reproject here)
cube13co._wcs = cube18co._wcs = cube303.wcs
cube13co.mask._wcs = cube18co.mask._wcs = cube303.wcs
if line == '303':
maincube = cube303
elif line == '321':
maincube = cube321
else:
raise ValueError("Unrecognized line: {0}".format(line))
# Prepare an array to hold the fitted temperatures
tcubedata = np.empty(maincube.shape, dtype='float32')
tcubedata[:] = np.nan
tcubeleafdata = np.empty(maincube.shape, dtype='float32')
tcubeleafdata[:] = np.nan
nbad = 0
catalog = ppv_catalog(dend, metadata)
pb = ProgressBar(len(catalog))
for ii,row in enumerate(catalog):
structure = dend[row['_idx']]
assert structure.idx == row['_idx'] == ii
dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cube303.wcs)
dend_inds = structure.indices()
view = (slice(dend_inds[0].min(), dend_inds[0].max()+1),
slice(dend_inds[1].min(), dend_inds[1].max()+1),
slice(dend_inds[2].min(), dend_inds[2].max()+1),)
#view2 = cube303.subcube_slices_from_mask(dend_obj_mask)
submask = dend_obj_mask[view]
#assert np.count_nonzero(submask.include()) == np.count_nonzero(dend_obj_mask.include())
sn = sncube[view].with_mask(submask)
sntot = sn.sum().value
#np.testing.assert_almost_equal(sntot, structure.values().sum(), decimal=0)
c303 = cube303[view].with_mask(submask)
c321 = cube321[view].with_mask(submask)
co13sum = cube13co[view].with_mask(submask).sum().value
co18sum = cube18co[view].with_mask(submask).sum().value
if hasattr(co13sum,'__len__'):
raise TypeError(".sum() applied to an array has yielded a non scalar.")
npix = submask.include().sum()
assert npix == structure.get_npix()
Stot303 = c303.sum().value
if np.isnan(Stot303):
raise ValueError("NaN in cube. This can't happen: the data from "
"which the dendrogram was derived can't have "
"NaN pixels.")
Smax303 = c303.max().value
Smin303 = c303.min().value
Stot321 = c321.sum().value
if npix == 0:
raise ValueError("npix=0. This is impossible.")
Smean303 = Stot303/npix
if Stot303 <= 0 and line=='303':
raise ValueError("The 303 flux is <=0. This isn't possible because "
"the dendrogram was derived from the 303 data with a "
"non-zero threshold.")
elif Stot303 <= 0 and line=='321':
Stot303 = 0
Smean303 = 0
elif Stot321 <= 0 and line=='321':
raise ValueError("The 321 flux is <=0. This isn't possible because "
"the dendrogram was derived from the 321 data with a "
"non-zero threshold.")
if np.isnan(Stot321):
raise ValueError("NaN in 321 line")
Smean321 = Stot321/npix
#error = (noise_cube[view][submask.include()]).sum() / submask.include().sum()**0.5
var = ((noise_cube[dend_obj_mask.include()]**2).sum() / npix**2)
error = var**0.5
if np.isnan(error):
raise ValueError("error is nan: this is impossible by definition.")
if line == '321' and Stot303 == 0:
r321303 = np.nan
er321303 = np.nan
elif Stot321 < 0:
r321303 = error / Smean303
er321303 = (r321303**2 * (var/Smean303**2 + 1))**0.5
else:
r321303 = Stot321 / Stot303
er321303 = (r321303**2 * (var/Smean303**2 + var/Smean321**2))**0.5
for c in columns:
assert len(columns[c]) == ii
columns['index'].append(row['_idx'])
columns['s_ntotal'].append(sntot)
columns['Stot303'].append(Stot303)
columns['Smax303'].append(Smax303)
columns['Smin303'].append(Smin303)
columns['Stot321'].append(Stot321)
columns['Smean303'].append(Smean303)
columns['Smean321'].append(Smean321)
columns['npix'].append(npix)
columns['e303'].append(error)
columns['e321'].append(error)
columns['r321303'].append(r321303)
columns['er321303'].append(er321303)
columns['13cosum'].append(co13sum)
columns['c18osum'].append(co18sum)
columns['13comean'].append(co13sum/npix)
columns['c18omean'].append(co18sum/npix)
columns['is_leaf'].append(structure.is_leaf)
columns['parent'].append(structure.parent.idx if structure.parent else -1)
columns['root'].append(get_root(structure).idx)
s_main = maincube._data[dend_inds]
x,y,z = maincube.world[dend_inds]
lon = ((z.value-(360*(z.value>180)))*s_main).sum()/s_main.sum()
lat = (y*s_main).sum()/s_main.sum()
vel = (x*s_main).sum()/s_main.sum()
columns['lon'].append(lon)
columns['lat'].append(lat.value)
columns['vcen'].append(vel.value)
mask2d = dend_obj_mask.include().max(axis=0)[view[1:]]
logh2column = np.log10(np.nanmean(column_regridded.data[view[1:]][mask2d]) * 1e22)
if np.isnan(logh2column):
log.info("Source #{0} has NaNs".format(ii))
logh2column = 24
elogh2column = elogabundance
columns['higaldusttem'].append(np.nanmean(dusttem_regridded.data[view[1:]][mask2d]))
r_arcsec = row['radius']*u.arcsec
reff = (r_arcsec*(8.5*u.kpc)).to(u.pc, u.dimensionless_angles())
mass = ((10**logh2column*u.cm**-2)*np.pi*reff**2*2.8*constants.m_p).to(u.M_sun)
density = (mass/(4/3.*np.pi*reff**3)/constants.m_p/2.8).to(u.cm**-3)
columns['reff'].append(reff.value)
columns['dustmass'].append(mass.value)
columns['dustmindens'].append(density.value)
mindens = np.log10(density.value)
if mindens < 3:
mindens = 3
if (r321303 < 0 or np.isnan(r321303)) and line != '321':
raise ValueError("Ratio <0: This can't happen any more because "
"if either num/denom is <0, an exception is "
"raised earlier")
#for k in columns:
# if k not in obs_keys:
# columns[k].append(np.nan)
elif (r321303 < 0 or np.isnan(r321303)) and line == '321':
for k in keys:
columns[k].append(np.nan)
else:
# Replace negatives for fitting
if Smean321 <= 0:
Smean321 = error
mf.set_constraints(ratio321303=r321303, eratio321303=er321303,
#ratio321322=ratio2, eratio321322=eratio2,
logh2column=logh2column, elogh2column=elogh2column,
logabundance=logabundance, elogabundance=elogabundance,
taline303=Smean303, etaline303=error,
taline321=Smean321, etaline321=error,
mindens=mindens,
linewidth=10)
row_data = mf.get_parconstraints()
row_data['ratio321303'] = r321303
row_data['eratio321303'] = er321303
for k in row_data:
columns[k].append(row_data[k])
# Exclude bad velocities from cubes
if row['v_cen'] < -80e3 or row['v_cen'] > 180e3:
# Skip: there is no real structure down here
nbad += 1
is_bad = True
else:
is_bad = False
tcubedata[dend_obj_mask.include()] = row_data['expected_temperature']
if structure.is_leaf:
tcubeleafdata[dend_obj_mask.include()] = row_data['expected_temperature']
columns['bad'].append(is_bad)
width = row['v_rms']*u.km/u.s
lengthscale = reff
#REMOVED in favor of despotic version done in dendrograms.py
# we use the analytic version here; the despotic version is
# computed elsewhere (with appropriate gcor factors)
#columns['tkin_turb'].append(heating.tkin_all(10**row_data['density_chi2']*u.cm**-3,
# width,
# lengthscale,
# width/lengthscale,
# columns['higaldusttem'][-1]*u.K,
# crir=0./u.s))
if len(set(len(c) for k,c in columns.items())) != 1:
print("Columns are different lengths. This is not allowed.")
import ipdb; ipdb.set_trace()
for c in columns:
assert len(columns[c]) == ii+1
if plot_some and not is_bad and (ii-nbad % 100 == 0 or ii-nbad < 50):
try:
log.info("T: [{tmin1sig_chi2:7.2f},{expected_temperature:7.2f},{tmax1sig_chi2:7.2f}]"
" R={ratio321303:8.4f}+/-{eratio321303:8.4f}"
" Smean303={Smean303:8.4f} +/- {e303:8.4f}"
" Stot303={Stot303:8.2e} npix={npix:6d}"
.format(Smean303=Smean303, Stot303=Stot303,
npix=npix, e303=error, **row_data))
pl.figure(1)
pl.clf()
mf.denstemplot()
pl.savefig(fpath("dendrotem/diagnostics/{0}_{1}.png".format(suffix,ii)))
pl.figure(2).clf()
mf.parplot1d_all(levels=[0.68268949213708585])
pl.savefig(fpath("dendrotem/diagnostics/1dplot{0}_{1}.png".format(suffix,ii)))
pl.draw()
pl.show()
except Exception as ex:
print ex
pass
else:
pb.update(ii+1)
if last_index is not None and ii >= last_index:
break
if last_index is not None:
catalog = catalog[:last_index+1]
for k in columns:
if k not in catalog.keys():
catalog.add_column(table.Column(name=k, data=columns[k]))
for mid,lo,hi,letter in (('expected_temperature','tmin1sig_chi2','tmax1sig_chi2','t'),
('expected_density','dmin1sig_chi2','dmax1sig_chi2','d'),
('expected_column','cmin1sig_chi2','cmax1sig_chi2','c')):
catalog.add_column(table.Column(name='elo_'+letter,
data=catalog[mid]-catalog[lo]))
catalog.add_column(table.Column(name='ehi_'+letter,
data=catalog[hi]-catalog[mid]))
if write:
catalog.write(tpath('PPV_H2CO_Temperature{0}.ipac'.format(suffix)), format='ascii.ipac')
# Note that there are overlaps in the catalog, which means that ORDER MATTERS
# in the above loop. I haven't yet checked whether large scale overwrites
# small or vice-versa; it may be that both views of the data are interesting.
tcube = SpectralCube(data=tcubedata, wcs=cube303.wcs,
mask=cube303.mask, meta={'unit':'K'},
header=cube303.header,
)
tcubeleaf = SpectralCube(data=tcubeleafdata, wcs=cube303.wcs,
mask=cube303.mask, meta={'unit':'K'},
header=cube303.header,
)
if write:
log.info("Writing TemperatureCube")
outpath = 'TemperatureCube_DendrogramObjects{0}.fits'
tcube.write(hpath(outpath.format(suffix)),
overwrite=True)
outpath_leaf = 'TemperatureCube_DendrogramObjects{0}_leaves.fits'
tcubeleaf.write(hpath(outpath_leaf.format(suffix)),
overwrite=True)
return catalog, tcube
relative=True,
color='r',
horizontalalignment='left')
F.add_label(0.05,
0.91,
glabel,
relative=True,
color='g',
horizontalalignment='left')
F.add_label(0.05,
0.87,
blabel,
relative=True,
color='b',
horizontalalignment='left')
F.save(paths.fpath("W51e2_{0}_aplpy.png".format(name)))
F.save(paths.fpath("W51e2_{0}_aplpy.pdf".format(name)))
cmcontsrc = Table.read(
paths.vpath('tables/EVLA_VLA_PointSourcePhotometry.ipac'),
format='ascii.ipac')
cmok = (cmcontsrc['Frequency'] == 5.9) & (cmcontsrc['Epoch'] == '3')
cmcoords = coordinates.SkyCoord(cmcontsrc['gracen'][cmok],
cmcontsrc['gdeccen'][cmok],
frame='fk5')
core_phot_tbl = Table.read(paths.tpath("continuum_photometry.ipac"),
format='ascii.ipac')
cores = coordinates.SkyCoord(core_phot_tbl['RA'],
core_phot_tbl['Dec'],
frame='fk5')
print(name)
print("nn11 ({2}): {0} +/- {1}".format(nn11.mean(), nn11.std(),
distances.shape[0]))
print("nn11m1 ({2}): {0} +/- {1}".format(nn11m1.mean(), nn11m1.std(),
distancesm1.shape[0]))
print("nn11m10 ({2}): {0} +/- {1}".format(nn11m10.mean(), nn11m10.std(),
distancesm10.shape[0]))
pl.figure(6 + nplots * ii).clf()
pl.title(name)
pl.plot(distances.mean(axis=0), label="all")
pl.plot(distancesm1.mean(axis=0), label="M>1")
pl.plot(distancesm10.mean(axis=0), label="M>10")
pl.legend(loc='best')
pl.savefig(paths.fpath(
'nndensity_experiments/{0}_mean_neighbor_distance_sample_size_effect.pdf'
).format(name),
bbox_inches='tight')
pl.figure(1 + nplots * ii)
pl.clf()
pl.title("{0} random distribution".format(name))
pl.hist(nn11, label='Full sample', density=True,
bins=25) #np.linspace(gridlims[0], gridlims[1], 20))
pl.hist(nn11m1, label='>1 Msun', alpha=0.5, density=True,
bins=25) #np.linspace(gridlims[0], gridlims[1], 20))
pl.hist(nn11m10, label='>10 Msun', alpha=0.5, density=True,
bins=25) #np.linspace(gridlims[0], gridlims[1], 20))
pl.legend(loc='best')
pl.savefig(paths.fpath(
'nndensity_experiments/{0}_mean_neighbor_distance_histogram.pdf').
fig5.clf()
Fsn = aplpy.FITSFigure(snhdu, convention='calabretta', figure=fig5)
Fsn.show_grayscale(vmin=0, vmax=10, stretch='linear', invert=True)
Fsn.add_colorbar()
Fsn.colorbar.set_axis_label_text('Peak S/N')
Fsn.recenter(**recen)
Fsn.axis_labels.set_xtext(r'Galactic Longitude')
Fsn.tick_labels.set_xformat('d.dd')
Fsn.refresh()
Fsn._ax1.set_ylabel("$V_{LSR} (\mathrm{km\ s}^{-1})$")
Fsn._ax1.set_yticklabels([(str(int(x.get_text()) / 1000))
for x in Fsn._ax1.get_yticklabels()])
Fsn.refresh()
Fsn.save(fpath("big_maps/pv_peaksn{0}.pdf".format(smooth)))
for ftemplate, outtype in toloop:
log.info("Starting " + ftemplate.format(smooth))
if 'pv' not in ftemplate:
cube = SpectralCube.read(hpath(ftemplate.format(smooth)))
if weight:
wcube = (SpectralCube.read(
hpath('APEX_H2CO_303_202_smooth_bl.fits'))
if 'smooth' in smooth else SpectralCube.read(
hpath('APEX_H2CO_303_202_bl.fits')))
mcube = (SpectralCube.read(
hpath('APEX_H2CO_303_202_smooth_bl_mask.fits'))
if 'smooth' in smooth else SpectralCube.read(
):
hdu = fits.open(
paths.dpath('12m/moments/CH3OH_{0}_cutout_temperaturemap.fits'.format(
sourcename)))
tmap = hdu[0].data
header = hdu[0].header
mywcs = wcs.WCS(header)
crd = coordinates.SkyCoord(center[0],
center[1],
frame='fk5',
unit=(u.hour, u.deg))
pixcen = mywcs.wcs_world2pix(crd.ra.deg, crd.dec.deg, 0)
weights = ((tmap > 0) & (tmap < 1000)).astype('float')
nr, bins, rprof = image_tools.radialprofile.azimuthalAverage(
tmap, binsize=1.0, weights=weights, center=pixcen, return_nr=True)
mywcs = wcs.WCS(header)
pixscale = (mywcs.pixel_scale_matrix.diagonal()**2).sum()**0.5
pl.figure(4).clf()
pl.plot(bins * pixscale * 3600, rprof)
pl.ylim(0, 1000)
pl.xlim(0, 2.5)
pl.xlabel("Radius (arcsec)")
pl.ylabel("Average Temperature (K)")
pl.savefig(
paths.fpath(
"chemistry/ch3oh_temperature_radial_profile_{0}.png".format(
sourcename)))
data = (cutout_e2 * u.beam).to(u.K,
beam_e2e8.jtok_equiv(masscalc.centerfreq)).value
norm = astropy.visualization.ImageNormalize(
data, stretch=astropy.visualization.AsinhStretch())
im = ax.imshow(data,
origin='lower',
interpolation='none',
cmap='gray',
norm=norm)
ax.contour(struct.get_mask(), levels=[0.5], colors=['orange'], origin='lower')
ax.contour(trunk.get_mask(), levels=[0.5], colors=['red'], origin='lower')
ax.set_xlabel("Right Ascension")
ax.set_ylabel("Declination")
cb = fig1.colorbar(mappable=im)
cb.set_label("$T_B$ [K]")
fig1.savefig(paths.fpath("longbaseline/e2cutout_contours_for_masscalc.pdf"),
bbox_inches='tight')
print("e2 upper contour Jy, K: {0}, {1}, {2}%".format(
struct.vmin * u.Jy,
u.Quantity(struct.vmin, u.Jy).to(u.K, beam_e2e8.jtok_equiv(226 * u.GHz)),
struct.vmin / struct.values().max() * 100,
))
print("e2 lower contour Jy, K: {0}, {1}, {2}%".format(
trunk.vmin * u.Jy,
u.Quantity(trunk.vmin, u.Jy).to(u.K, beam_e2e8.jtok_equiv(226 * u.GHz)),
trunk.vmin / struct.values().max() * 100))
dende8 = astrodendro.Dendrogram.compute(cutout_e8.value,
min_value=0.0005,
min_delta=0.00001,
negamp=False,
limited=[(True, False), (False, False), (True, False)])
thisspec.baseline(excludefit=True, order=2, subtract=True)
thisspec.specfit(fittype='gaussian',
guesses='moments',
negamp=False,
limited=[(True, False), (False, False), (True, False)])
log.info(thisspec.specname +
" fitting: {0}".format(thisspec.specfit.parinfo))
thisspec.plotter.ymin -= 0.0005
thisspec.specfit.plotresiduals(axis=thisspec.plotter.axis,
clear=False,
yoffset=-0.005,
label=False)
thisspec.plotter.savefig(
paths.fpath('spectra/emission/' + thisspec.specname +
"_h2co22emisson_fit.png"),
bbox_inches='tight')
thisspec.plotter(xmin=30, xmax=90, errstyle='fill')
# Jy -> mJy
thisspec.data *= 1e3
thisspec.error *= 1e3
#thisspec.unit = '$T_B$ (K)'
thisspec.unit = 'mJy/beam'
thisspec.plotter(errstyle='fill')
ax2 = thisspec.plotter.axis.twinx()
ax2.set_ylim(*(np.array(thisspec.plotter.axis.get_ylim()) *
thisspec.header['JYTOK'] / 1e3))
ax2.set_ylabel("$T_B$ (K)")
thisspec.plotter.savefig(
paths.fpath('spectra/emission/' + thisspec.specname +
rgb_mm_im = np.zeros(nir_im.shape + (3, ))
rgb_mm_im[:, :, blue] = np.nan_to_num(monochrome_mm_im)
#rgb_mm_im[:,:,blue] = monochrome_mm_im.filled()
hsv_mm_im = rgb_to_hsv(rgb_mm_im)
hsv_mm_im[:, :, hue] = 20 / 360.
rgb_mm_im = hsv_to_rgb(hsv_mm_im)
rgb_im[:, :, :alpha] += rgb_mm_im
#rgb_im[rgb_im>1] = 1
#rgb_im /= rgb_im.max()
avm = pyavm.AVM.from_header(outhdr)
im = PIL.Image.fromarray((rgb_im[:, :, :alpha] / rgb_im[:, :, :alpha].max() *
255).astype('uint8')[::-1, :])
outname = fpath("rgb_irs2_default.png")
im.save(outname)
avm.embed(outname, outname)
im = ImageEnhance.Contrast(im).enhance(1.5)
outname = fpath("rgb_irs2_contrast.png")
im.save(outname)
avm.embed(outname, outname)
im = ImageEnhance.Brightness(im).enhance(1.5)
outname = fpath("rgb_irs2_brightness.png")
im.save(outname)
avm.embed(outname, outname)
#im = ImageEnhance.Brightness(im).enhance(1.5)
#outname = paths.fpath("rgb_irs2_brightness_1.5.png")
#im.save(outname)
#avm.embed(outname, outname)
if len(cax.yaxis.get_ticklabels()) < 2:
ticks = np.logspace(np.log10(vmin), np.log10(vmax), 5)
cax.yaxis.set_ticks(ticks)
cax.yaxis.set_ticklabels(["{0:0.2g}".format(x) for x in ticks])
cax.set_ylabel(method_label[method])
cb.ax.yaxis.set_label_position('right')
ax.set_xlabel("Galactic Longitude")
ax.set_ylabel("Galactic Latitude")
tr = ax.get_transform(dustcolwcs)
con = ax.contour(dustcoldata, levels=[5], colors=[(0,0,0,0.5)],
zorder=15, alpha=0.5, linewidths=[0.5], transform=tr)
ax.axis(limits)
pl.savefig(fpath('integrated/{0}'.format(outname.replace(".fits",".pdf"))),
bbox_inches='tight')
for c in con.collections: c.set_visible(False)
labels = pyregion.open(rpath('ridge_names.reg'))
PC, TC = ds9(labels, im.header, text_offset=0)
#PC.add_to_axes(ax)
TC = aplpyregions.ArtistCollection([x for x in TC.artistlist if isinstance(x, matplotlib.text.Annotation)])
TC.add_to_axes(ax)
pl.savefig(fpath('integrated/{0}'.format(outname.replace(".fits","_labeled.pdf"))),
bbox_inches='tight')
for c in con.collections: c.set_visible(True)
ax.axis(limits)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
pl.figure(1).clf()
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
if hasattr(spectrum, 'beams'):
include = np.isfinite(spectrum) & np.array([(bm.major < 1*u.arcsec) &
(bm.minor < 1*u.arcsec)
for bm in spectrum.beams])
avg_beam = spectral_cube.cube_utils.average_beams(spectrum.beams,
includemask=include)
spectrum.meta['beam'] = avg_beam
spectrum.write(paths.spath('{3}_radial_bin_{0:0.2f}to{1:0.2f}_spw{2}.fits'
.format(bins[0], bins[1], spw, pfx)),
overwrite=True
)
pl.plot(spectrum.spectral_axis.to(u.GHz).value, spectrum.value,
label='{0:0.1f}-{1:0.1f}'.format(*bins))
pl.xlabel("Frequency (GHz)")
pl.ylabel("Intensity (Jy)")
pl.gca().ticklabel_format(useOffset=False)
pl.gca().get_xaxis().get_major_formatter().set_scientific(False)
pl.gca().get_yaxis().get_major_formatter().set_scientific(False)
pl.legend(loc='best')
pl.savefig(paths.fpath('radial_spectra/{1}_radial_spectra_spw{0}.png'
.format(spw, pfx)))
for source in ('e8','north','e2',):
matplotlib.pyplot.figure(1).clf()
F1 = aplpy.FITSFigure(paths.dpath('chemslices/chemical_m0_slabs_{0}_CH3OH1029-936_merge.fits'.format(source)),
figure=matplotlib.pyplot.figure(1))
F1.show_grayscale(invert=True, vmax=1100, vmin=-20)
F1.show_contour(paths.dpath('W51_te_continuum_best.fits'),
colors=['r']*11,
levels=[0.015, 0.0256944, 0.0577778, 0.11125, 0.186111,
0.282361, 0.4, ])
F1.add_scalebar((0.025*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
F1.scalebar.set_label('5000 au / 0.025 pc')
F1.scalebar.set_color('k')
F1.save(paths.fpath("{0}_continuum_contours_on_ch3oh1029.png".format(source)))
matplotlib.pyplot.figure(2).clf()
F = aplpy.FITSFigure(paths.dpath('W51_te_continuum_best.fits'),
figure=matplotlib.pyplot.figure(2))
if source == 'e2': # TODO: mapping for each source
F.recenter(290.93315, 14.5097, 2.8/3600.)
F.show_grayscale(invert=True, vmax=0.43, vmin=-0.01, stretch='arcsinh')
F.show_contour(paths.dpath('chemslices/chemical_m0_slabs_{0}_CH3OH1029-936_merge.fits'.format(source)),
colors=['b']*11,
levels=np.linspace(200,1100,6), layer='CH3OH')
#F.ticks.set_xspacing(0.0005)
F.add_scalebar((0.025*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
F.scalebar.set_label('5000 au / 0.025 pc')
F.scalebar.set_color('k')
]])
],
color='r')
FF.recenter(pars['cx'], pars['cv'], width=pars['wx'], height=pars['wv'])
# show() is unfortunately required before the text labels are set
pl.draw()
pl.show()
# may have to find a better way to force this to work:
FF._ax1.set_yticklabels(
[str(float(L.get_text()) / 1e3) for L in FF._ax1.get_yticklabels()])
FF._ax1.set_ylabel("Velocity (km/s)")
FF._ax1.set_xticklabels(
[str(float(L.get_text()) * 3600) for L in FF._ax1.get_xticklabels()])
FF._ax1.set_xlabel("Offset (arcsec)")
FF.save(paths.fpath('outflows_pv/' + outname.format(extension='png')))
h2pixels = pars['path'].sample_points(1.0, h2wcs)
h2velos = [
h2velomap[0].data[x, y] for y, x in zip(*h2pixels) if x > 0 and y > 0
and x < h2velomap[0].data.shape[1] and y < h2velomap[0].data.shape[0]
]
h2radec = [
h2wcs.wcs_pix2world(x, y, 0) for y, x in zip(*h2pixels)
if x > 0 and y > 0 and x < h2velomap[0].data.shape[1]
and y < h2velomap[0].data.shape[0]
]
h2offset = [offset_to_point(ra, dec, pars['path']) for ra, dec in h2radec]
#h2widths = h2velowidthmap[h2pixels]
if h2offset:
FF.show_markers(h2offset, (np.array(h2velos) * 1000).tolist(),
for i in range(Nrows):
for j in range(Ncols):
if i == 0:
axes[i,j].xaxis.set_ticks_position('top')
pl.setp(axes[i,j].get_xticklabels(), visible=False)
axes[i,j].xaxis.set_ticklabels([])
elif i == Nrows-1:
axes[i,j].xaxis.set_ticks_position('bottom')
pl.setp(axes[i,j].get_xticklabels(), visible=True)
else:
axes[i,j].xaxis.set_ticks_position('none')
pl.setp(axes[i,j].get_xticklabels(), visible=False)
axes[i,j].xaxis.set_ticklabels([])
if j == 0:
axes[i,j].yaxis.set_ticks_position('left')
elif j == Ncols-1:
axes[i,j].yaxis.set_ticks_position('right')
pl.setp(axes[i,j].get_yticklabels(), visible=False)
axes[i,j].yaxis.set_ticklabels([])
else:
axes[i,j].yaxis.set_ticks_position('none')
pl.setp(axes[i,j].get_yticklabels(), visible=False)
axes[i,j].yaxis.set_ticklabels([])
pl.subplots_adjust(hspace=0,
wspace=0)
pl.savefig(paths.fpath('outflows/{0}_{1}_channelmaps.png'.format(sourcename,species)),
bbox_inches='tight')
if (plotnum - 1) >= (ny * (nx - 1)):
pl.xlabel("$E_u$ [K]")
tl = pl.gca().get_yaxis().get_ticklabels()
xax = pl.gca().get_xaxis()
if (plotnum - 1) == (nx * ny - 1):
xax.set_ticks((0, 200, 400, 600, 800))
else:
xax.set_ticks((0, 200, 400, 600))
xax.set_tick_params(labelsize=14)
else:
pl.gca().get_xaxis().set_ticklabels([])
pl.xlabel("")
#pl.legend(loc='best', fontsize='small')
pl.subplots_adjust(hspace=0, wspace=0)
pl.savefig(
paths.fpath("chemistry/ch3oh_rotation_diagrams_{0}.png".format(
sourcename)))
if True:
tmap, Nmap = fit_all_tex(xaxis,
cube,
cubefrequencies,
indices,
degeneracies,
ecube=ecube,
replace_bad=replace_bad)
pl.figure(1).clf()
pl.imshow(tmap, vmin=0, vmax=600, cmap='hot')
cb = pl.colorbar()
cb.set_label("Temperature (K)")
pl.savefig(
except AssertionError:
print("Label is {0} instead of Velocity (km / s)".format(ss.plotter.xlabel))
print("Drawn label is {0}".format(ss.plotter.axis.get_xlabel()))
ss.specfit(fittype='gaussian',
guesses=[1,55,10],
limited=[(True,False),(False,False),(True,False)])
log.info(ss.specname+" fitting: {0}".format(ss.specfit.parinfo))
ss.plotter.ymin -= 0.3
ss.plotter.label(ylabel='$S_\\nu$ (mJy beam$^{-1}$)')
ax2 = ss.plotter.axis.twinx()
ax2.set_ylim(*(np.array(ss.plotter.axis.get_ylim()) * ss.header['JYTOK']/1e3))
ax2.set_ylabel("$T_B$ (K)")
ss.specfit.plotresiduals(axis=ss.plotter.axis,clear=False,yoffset=-0.3,label=False)
ss.plotter.savefig(paths.fpath('spectra/h77/'+ss.specname+"_h77a_fit.png"),
bbox_inches='tight')
pl.figure(ii).clf()
ss.plotter(xmin=-10,xmax=120, figure=pl.figure(ii), axis=pl.subplot(2,1,1))
ss.plotter.ymin -= 0.3
ss.plotter.label(ylabel='$S_\\nu$ (mJy beam$^{-1}$)')
ss.specfit.plot_fit()
ax2 = ss.plotter.axis.twinx()
ax2.set_ylim(*(np.array(ss.plotter.axis.get_ylim()) * ss.header['JYTOK']/1e3))
ax2.set_ylabel("$T_B$ (K)")
ss.specfit.plotresiduals(axis=ss.plotter.axis,clear=False,yoffset=-0.3,label=False)
ss_he = ss.copy()
ax1.lines[-1],
Line2D([0], [0], linestyle=':', color='k', linewidth=2),
Line2D([0], [0], linestyle='--', color='k', linewidth=2),
Line2D([0], [0], linestyle='-', color='k', linewidth=2),
), (
'$20$ K',
'$50$ K',
'$100$ K',
'$10^{13}$ cm$^{-2}$',
'$10^{14}$ cm$^{-2}$',
'$10^{15}$ cm$^{-2}$',
),
loc='best')
#ax1.set_title("$N(p-H_2CO)=10^{14}$")
fig1.savefig(paths.fpath('radex_ratio_lines_vs_density.pdf'),
bbox_inches='tight')
crange1 = np.logspace(12, 16.5, 100)
fortho = 0.1
fig2 = pl.figure(2)
fig2.clf()
ax2 = fig2.gca()
for density, linestyle in zip((1e3, 1e5, 1e7), (':', '--', '-')):
for temperature, color in zip((20, 50, 100), ('r', 'b', 'purple')):
tables1 = [
RR(density={
'oH2': density * fortho,
'pH2': density * (1 - fortho)
},
F.show_grayscale(stretch='log',vmin=-1,vmid=-1.5,vmax=1e2,invert=True)
#e1 = coordinates.ICRS(290.93263,14.50745,unit=('deg','deg'))
#F.recenter(e1.ra.value,e1.dec.value,width=1/60.,height=1/60.)
#F.recenter(290.92633,14.514769,radius=1.4/60.)
F.add_scalebar(length=((0.5 * u.pc)/distance*u.radian).to(u.degree).value)
F.recenter(290.91669,14.518151,radius=8./3600.)
set_tight_ticks(F)
F.scalebar.set_length(((0.1 * u.pc)/distance*u.radian).to(u.degree).value)
F.scalebar.set_label('0.1 pc')
F.scalebar.set_color('black')
F.scalebar.set_linewidth(3)
F.scalebar.set_font_size(20)
F.save(fpath('IRS2_NACO_Kband.pdf'), dpi=150)
radiohdu = fits.open(dpath('W51Ku_BDarray_continuum_2048_both_uniform.hires.clean.image.fits'))
for ii in [3,4]:
for kk in ['CRVAL','CTYPE','CDELT','CRPIX','CUNIT','NAXIS']:
k = (kk+'{0}').format(str(ii))
if k in radiohdu[0].header:
del radiohdu[0].header[k]
for k in set(radiohdu[0].header.keys()):
if k[:2] == 'PC':
del radiohdu[0].header[k]
radiohdu[0].header['NAXIS'] = 2
radiohdu[0].data = radiohdu[0].data.squeeze()
F.show_contour(radiohdu,
levels=np.logspace(np.log10(0.001), np.log10(0.07), 12),
fit.plot_ccdf(color='k')
fit.power_law.plot_ccdf(color='r', linestyle='--')
ax2.set_ylabel("Fraction of sources")
ax3 = fig2.add_subplot(212)
fit = powerlaw.Fit(dendro_merge['peak_cont_flux'])
# doesn't work at all fit.plot_pdf(color='k')
ax3.hist(dendro_merge['peak_cont_flux'], bins=np.logspace(-3,-0.5,15),
color='k', facecolor='none', histtype='step')
ax3.set_xscale('log')
fit.power_law.plot_pdf(color='r', linestyle='--')
ax3.set_ylim(0.3, 51)
ax3.set_xlabel("Peak flux density (Jy/beam)")
ax3.set_ylabel("Number of sources")
fig2.savefig(paths.fpath('coreplots/dendro_flux_powerlaw_histogram_fit.png'))
print("Fit parameters: alpha={0}".format(fit.power_law.alpha))
radii = (0.2,0.4,0.6,0.8,1.0,1.5)*u.arcsec
lines = np.array([[row['peak_cont_flux']] + [row['cont_flux{0}arcsec'.format(rad).replace(".","p")]
for rad in radii.value]
for row in dendro_merge])
pradii = (0.0,0.2,0.4,0.6,0.8,1.0,1.5)
pl.clf()
pl.plot(pradii, lines.T)
pl.plot(pradii, lines[corelike].T)
pl.xlabel("Aperture Radius (\")")
pl.ylabel("Flux (Jy)")
pl.savefig(paths.fpath('coreplots/flux_vs_aperture_radius_alldendrosources.png'))
fig.clf()
FF = aplpy.FITSFigure(paths.dpath('12m/continuum/selfcal_allspw_selfcal_3_mfs_deeper_r0.0.image.pbcor.fits'),
figure=fig)
FF.show_colorscale(cmap='bone_r', vmin=-0.005, vmax=0.025, stretch='log', vmid=-0.006)
FF.recenter(center.ra.deg, center.dec.deg, radius=0.0025)
FF.show_colorbar()
FF.add_scalebar((0.1*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
FF.scalebar.set_color('w')
FF.scalebar.set_linewidth(4)
FF.scalebar.set_label("0.1 pc")
FF.scalebar.set_font_size(20)
FF.add_beam()
FF.beam.set_facecolor('none')
FF.beam.set_linewidth(2)
FF.beam.set_edgecolor('w')
FF.save(paths.fpath("e5_bubble.png"))
fig = pl.figure(1)
fig.clf()
FF = aplpy.FITSFigure(paths.dpath('12m/continuum/selfcal_allspw_selfcal_3_mfs_deeper_r2.0.image.pbcor.fits'),
figure=fig)
FF.show_colorscale(cmap='bone_r', vmin=-0.015, vmax=0.095, stretch='log', vmid=-0.016)
FF.recenter(center.ra.deg, center.dec.deg, radius=0.0025)
FF.show_colorbar()
FF.add_scalebar((0.1*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
FF.scalebar.set_color('w')
FF.scalebar.set_linewidth(4)
FF.scalebar.set_label("0.1 pc")
FF.scalebar.set_font_size(20)
FF.show_contour(ku_fn, levels=[0.0015, 0.003, 0.006], colors=['w']*5)
FF.add_beam()
m0 = vcube.with_mask(vcube>0.05*u.Jy).moment0()
m1 = vcube.with_mask(vcube>0.05*u.Jy).moment1()
m2 = vcube.with_mask(vcube>0.05*u.Jy).moment2()
m0.quicklook()
m0.hdu.writeto(m0fn, clobber=True)
m1.quicklook()
m1.hdu.writeto(m1fn, clobber=True)
m2.quicklook()
m2.hdu.writeto(m2fn, clobber=True)
try:
m0.FITSFigure.save(fpath("moments/{0}_moment0.png".format(fname)))
m1.FITSFigure.show_colorscale(cmap='viridis', vmin=45, vmax=68)
m1.FITSFigure.show_contour(m0.hdu, levels=[4], colors=['k'])
m1.FITSFigure.save(fpath("moments/{0}_moment1.png".format(fname)))
m2.FITSFigure.show_colorscale(cmap='viridis', vmin=0, vmax=30)
m2.FITSFigure.show_contour(m0.hdu, levels=[4], colors=['k'])
m2.FITSFigure.save(fpath("moments/{0}_moment2.png".format(fname)))
for ra,dec,rad,name in ((290.93253, 14.508016, 0.00311407, 'e2e8'),
(290.9118, 14.512366, 0.00311407, 'southeast'),
(290.9166, 14.518094, 0.00311407, 'north')):
m0.FITSFigure.recenter(ra, dec, rad)
m0.FITSFigure.save(fpath("moments/{0}_{1}zoom_moment0.png".format(fname,name)))
m1.FITSFigure.recenter(ra, dec, rad)
m1.FITSFigure.save(fpath("moments/{0}_{1}zoom_moment1.png".format(fname,name)))
def multiscale_fit(pcube,
centerx,
centery,
offset_scale=0.3,
savedir=None,
savepre=None,
exclude=(236, 323, 539, 654, 972, 1049, 1180, 1238),
radii=(2, 3, 4, 5, 6, 7, 8, 9),
guesses=[0.84, 37.9, 6.5, 0.5, 0.8, 1.0]):
pcube.plotter.figure = pl.figure(1)
pcube.plotter.axis = pl.figure(1).gca()
pcube.specfit.Registry.add_fitter('h2co_simple',
simple_fitter2,
6,
multisingle='multi')
sp = pcube.get_spectrum(centerx, centery)
sp.plotter.figure = pl.figure(1)
sp.plotter.axis = pl.figure(1).gca()
fitspec(sp, exclude, guesses)
sp.specfit.plotresiduals(axis=pcube.plotter.axis,
yoffset=-sp.specfit.parinfo[0].value *
offset_scale,
clear=False,
color='#444444',
label=False)
sp.plotter.axis.set_ylim(
-5 * sp.specfit.residuals.std() -
sp.specfit.parinfo[0].value * offset_scale,
pcube.plotter.axis.get_ylim()[1])
sp.plotter.savefig(paths.fpath('{0}/{1}_r0.pdf'.format(savedir, savepre)))
r1b = []
er1b = []
sigb = []
esigb = []
r1 = [sp.specfit.parinfo[3].value]
er1 = [sp.specfit.parinfo[3].error]
sig = [sp.specfit.parinfo[2].value]
esig = [sp.specfit.parinfo[2].error]
cen = [sp.specfit.parinfo[1].value]
ecen = [sp.specfit.parinfo[1].error]
mergefig = pl.figure(0)
mergefig.clf()
splast = sp
for ii, radius in enumerate(radii):
sp = pcube.get_apspec([centerx, centery, radius], wunit='pixel')
sp.plotter(figure=pl.figure(1))
fitspec(sp, exclude, guesses)
sp.specfit.plotresiduals(axis=pcube.plotter.axis,
yoffset=-sp.specfit.parinfo[0].value *
offset_scale,
clear=False,
color='#444444',
label=False)
sp.plotter.axis.set_ylim(
-5 * sp.specfit.residuals.std() -
sp.specfit.parinfo[0].value * offset_scale,
sp.plotter.axis.get_ylim()[1])
sp.plotter.savefig(
paths.fpath('{0}/{1}_r{2}.pdf'.format(savedir, savepre, radius)))
r1.append(sp.specfit.parinfo[3].value)
er1.append(sp.specfit.parinfo[3].error)
sig.append(sp.specfit.parinfo[2].value)
esig.append(sp.specfit.parinfo[2].error)
cen.append(sp.specfit.parinfo[1].value)
ecen.append(sp.specfit.parinfo[1].error)
spannulus = sp - splast
spannulus.plotter.figure = sp.plotter.figure
spannulus.plotter.axis = sp.plotter.axis
fitspec(spannulus, exclude, guesses=sp.specfit.parinfo.values)
spannulus.specfit.plotresiduals(
axis=pcube.plotter.axis,
yoffset=-spannulus.specfit.parinfo[0].value * offset_scale,
clear=False,
color='#444444',
label=False)
spannulus.plotter.axis.set_ylim(
-5 * spannulus.specfit.residuals.std() -
spannulus.specfit.parinfo[0].value * offset_scale,
spannulus.plotter.axis.get_ylim()[1])
spannulus.plotter.savefig(
paths.fpath('{0}/{1}_r{2}_annulus.pdf'.format(
savedir, savepre, radius)))
r1b.append(spannulus.specfit.parinfo[3].value)
er1b.append(spannulus.specfit.parinfo[3].error)
sigb.append(spannulus.specfit.parinfo[2].value)
esigb.append(spannulus.specfit.parinfo[2].error)
splast = sp.copy()
sp.smooth(4)
sp.plotter(figure=mergefig,
clear=False,
axis=mergefig.gca(),
color=pl.cm.spectral(ii / float(len(radii))),
label="{0:0.1f}".format(radius * 7.2))
sp.plotter.axis.relim()
sp.plotter.axis.autoscale()
pl.figure(mergefig.number)
pl.legend(loc='upper right', fontsize=16)
mergefig.savefig(
paths.fpath('{0}/{1}_mergefig.pdf'.format(savedir, savepre)))
pl.figure(2)
pl.clf()
ax1 = pl.subplot(2, 1, 1)
ax1.errorbar(np.arange(1, 10) * 7.2,
r1,
yerr=er1,
linestyle='none',
marker='s',
color='k')
ax1.set_xlim(0.5 * 7.2, 9.5 * 7.2)
#ax1.set_xlabel("Aperture Radius (arcseconds)")
ax1.set_ylabel("Ratio $3_{2,1}-2_{2,0} / 3_{0,3} - 2_{0,2}$")
ax1.xaxis.set_ticklabels([])
# Remove the bottom label (overlap)
tl = ax1.yaxis.get_ticklabels()
tl[0].set_visible(False)
ax1b = ax1.twinx()
# set the y limits to pwtem(the ratio limits)
ylim = ax1.get_ylim()
# pwtem can't go above 0.6; returns NaN above that
if ylim[1] > 0.599999:
ylim = (ylim[0], 0.599999)
ax1.set_ylim(*ylim)
ax1b.set_ylim(*pwtem(ylim))
ax1b.xaxis.set_ticklabels([])
ax1b.set_ylabel('Temperature (K)')
ax2 = pl.subplot(2, 1, 2)
pl.subplots_adjust(hspace=0)
ax2.errorbar(np.arange(1, 10) * 7.2,
sig,
yerr=esig,
linestyle='none',
marker='s',
color='k')
ax2.set_xlim(0.5 * 7.2, 9.5 * 7.2)
ax2.set_xlabel("Aperture Radius (arcseconds)")
ax2.set_ylabel("$\sigma$ (km s$^{-1}$)")
# Steve suggested adding a Mach number label, but this isn't possible:
# it would require a 3rd graph, since M ~ sigma/sqrt(T)
# ax2b = ax2.twinx()
# # set the y limits to the sigma limits
# ylim = ax2.get_ylim()
# ax2b.set_ylim()
# ax2b.xaxis.set_ticklabels([])
# ax2b.set_ylabel('Temperature (K)')
pl.savefig(
paths.fpath('{0}/{1}_ratio_vs_scale.pdf'.format(savedir, savepre)))
ax1.errorbar(np.arange(2, 10) * 7.2,
r1b,
yerr=er1b,
linestyle='none',
marker='s',
color='b',
zorder=-1,
alpha=0.5)
ax2.errorbar(np.arange(2, 10) * 7.2,
sigb,
yerr=esigb,
linestyle='none',
marker='s',
color='b',
zorder=-1,
alpha=0.5)
pl.savefig(
paths.fpath('{0}/{1}_ratio_vs_scale_annuli.pdf'.format(
savedir, savepre)))
return r1, er1, sig, esig, cen, ecen, r1b, er1b, sigb, esigb
import os
import paths
import glob
figstr = """
\Figure{{figures/pointsource_seds/{fname}}}
{{Point source photometry of {objname}. See Figure \\ref{{fig:d4sed}} for
details}}
{{fig:{objname}sed}}{{1}}{{7in}}\n\clearpage\n"""
with open(paths.texpath("ptsrcphotometryfigures.tex"), "w") as f:
for fn in glob.glob(paths.fpath("pointsource_seds/*png")):
fname = os.path.split(fn)[-1]
objname = fname.split("_")[0]
f.write(figstr.format(fname=fname, objname=objname))
for ii in range(1, plotnum):
ax = pl.subplot(7, 7, ii)
ymin = min(ax.get_ylim()[0], ymin)
ymax = max(ax.get_ylim()[1], ymax)
for ii in range(1, plotnum):
ax = pl.subplot(7, 7, ii)
#ax.set_ylim(ymin, ymax)
#if ii % 7 != 1:
ax.yaxis.set_ticklabels([])
ax.yaxis.set_label("")
if ii < plotnum - 7:
ax.xaxis.set_ticklabels([])
ax.xaxis.set_label("")
pl.savefig(paths.fpath('spectral_fits/{0}_linefits.png'.format(name)),
bbox_inches='tight',
bbox_extra_artists=[])
pl.figure(2).clf()
widths = line_table['{0}FittedWidth'.format(name)]
ewidths = line_table['{0}FittedWidthError'.format(name)]
labels = line_table['Species']
inds = np.argsort(widths)
mask = (widths[inds] > 0) & (widths[inds] < 5) & (
ewidths[inds] < widths[inds]) & (ewidths[inds] < 5)
pl.errorbar(x=np.arange(mask.sum()),
y=widths[inds][mask],
yerr=ewidths[inds][mask],
linestyle='none',
color='k')
tbl.add_column(Column(data=10**tbl['log10_Aij'], unit=u.s**-1, name='Aij'))
tbl.rename_column('Upper State Degeneracy', 'gu')
if __name__ == "__main__":
# investigate difference between splatalogue & Barton cats
KCl_diff = match_splat_barton(KCls, KCl[KCl['vu'] < 4])
NaCl_diff = match_splat_barton(NaCls, NaCl[NaCl['vu'] < 6])
import pylab as pl
pl.figure(1).clf()
pl.plot(KCl_diff['Freq_1'], KCl_diff['Splat-Barton'], '.')
pl.title("KCl")
pl.xlabel("Frequency (GHz)")
pl.ylabel("Splatalogue - Barton frequency difference (GHz)")
pl.savefig(paths.fpath('Splatalogue-Barton_comparison_KCl.pdf'))
pl.clf()
pl.plot(KCl_diff['Freq_1'],
(KCl_diff['Splat-Barton'].quantity / KCl_diff['Freq_1'].quantity *
constants.c).to(u.km / u.s), '.')
pl.title("KCl")
pl.xlabel("Frequency (GHz)")
pl.ylabel("Splatalogue - Barton frequency difference (km/s)")
pl.savefig(paths.fpath('Splatalogue-Barton_comparison_KCl_kms.pdf'))
pl.clf()
pl.plot(KCl_diff['EU_K'],
(KCl_diff['Splat-Barton'].quantity / KCl_diff['Freq_1'].quantity *
constants.c).to(u.km / u.s), '.')
pl.title("KCl")
fittable = table.Table.read(tpath("fitted_line_parameters.ipac"),
format='ascii.ipac')
fittable.add_columns([table.Column(name=name, dtype='float', length=len(fittable))
for name in ['temperature_chi2','tmin1sig_chi2','tmax1sig_chi2',
'expected_temperature',
'column_chi2','cmin1sig_chi2','cmax1sig_chi2',
'expected_column',
'density_chi2','dmin1sig_chi2','dmax1sig_chi2',
'expected_density',
'logh2column','elogh2column',
'logabundance','elogabundance',
'tkin_turb', 'reff_pc',
]])
if not os.path.exists(paths.fpath('param_fits')):
os.makedirs(paths.fpath('param_fits'))
nlevs = 4
#levels = [stats.norm.cdf(ii)-stats.norm.cdf(-ii)
# for ii in range(1,nlevs)]
ndof = 3
levels = ([0]+
[stats.chi2.ppf(stats.norm.cdf(ii)-stats.norm.cdf(-ii),
ndof)
for ii in range(1,nlevs)])
density_label = 'Density $n(\mathrm{H}_2)$ [log cm$^{-3}$]'
column_label = 'Column p-H$_2$CO [log cm$^{-2}$/(km s$^{-1}$ pc)]'
density_label_short = "$n(\mathrm{H}_2) (\mathrm{cm}^{-3})$"
column_label_short = "$N(\mathrm{p-H}_2\mathrm{CO}) (\mathrm{cm}^{-2})$"
for regfn, contfn, name in (
('w51north_protostars.reg', 'W51n_cont_uniform.image.tt0.pbcor.fits',
'NorthUniform'),
('w51north_protostars.reg', 'W51n.cont.image.allEB.fits',
'NorthRobust'),
('w51e_protostars.reg', 'W51e2_cont_uniform.image.tt0.pbcor.fits',
'W51eUniform'),
('w51e_protostars.reg', 'W51e2_cont_briggs.image.fits', 'W51eRobust'),
):
regs = regions.read_ds9(paths.rpath(regfn))
contfn = paths.dpath('longbaseline/' + contfn)
fit_data = gaussfit_catalog(
contfn,
regs,
radius=0.1 * u.arcsec,
prefix=name + "_",
max_radius_in_beams=5,
max_offset_in_beams=2,
savepath=paths.fpath('longbaseline/gaussfits'))
tbl = data_to_table(fit_data)
tbl.rename_column("chi2/n", "chi2_n")
tbl.write(paths.tpath("gaussian_fit_table_{0}.ipac".format(name)),
format='ascii.ipac',
overwrite=True)
sblength = 0.05*u.pc
F.add_scalebar(length=(sblength/distance*u.radian).to(u.degree).value)
F.scalebar.set_label(sblength)
F.scalebar.set_color('black')
F.scalebar.set_linewidth(3)
F.scalebar.set_font_size(20)
pixscale = np.abs(F._wcs.pixel_scale_matrix[1,1])
if name in annotations:
for (x,y), to_offset, from_offset, color, kwargs in annotations[name]:
dx = (from_offset[0]-to_offset[0]) / pixscale
dy = (from_offset[1]-to_offset[1]) / pixscale
F.show_arrows(x, y, dx, dy, color=color)
F.show_colorbar()
F.colorbar.set_axis_label_text("mJy/beam")
F.save(paths.fpath("diffuse/{0}_aplpy.png".format(name)))
fig = pl.figure(2)
fig.clf()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcsaxes)
im = ax.imshow(hdu.data.squeeze()*1e3, cmap=pl.cm.gray_r, origin='lower',
vmin=vmin, vmax=vmax,
norm=ImageNormalize(stretch=stretch))
(x1,y1),(x2,y2) = mywcs.wcs_world2pix(coord_limits, 0)
ax.set_xlim(x1,x2)
ax.set_ylim(y1,y2)
ra = ax.coords['ra']
ra.set_major_formatter('hh:mm:ss.s')
ra.set_ticks(spacing=spacing*u.deg)
dec = ax.coords['dec']
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]")
regions = pyregion.open(paths.rpath("hmcore_centroids.reg"))
make_rprof(regions, ploteach=True)
nplots = len(regions)
pl.figure(nplots * 3 + 2).savefig(
paths.fpath("cumulative_radial_flux_massivecores.png"))
pl.figure(nplots * 3 + 3).savefig(
paths.fpath("cumulative_radial_mass_massivecores.png"))
pl.figure(nplots * 3 + 4).savefig(
paths.fpath("cumulative_density_40K_massivecores.png"))
pl.figure(nplots * 3 + 5).savefig(
paths.fpath("azimuthalaverage_density_40K_massivecores.png"))
pl.figure(nplots * 3 + 6).savefig(
paths.fpath("azimuthalaverage_radial_mj_40K_massivecores.png"))
pl.figure(nplots * 3 + 7).savefig(
paths.fpath("azimuthalaverage_radial_mj_of_TCH3OH_massivecores.png"))
pl.figure(nplots * 3 + 8).savefig(
paths.fpath("cumulative_radial_mass_of_TCH3OH_massivecores.png"))
pl.figure(nplots * 3 + 9).savefig(
paths.fpath("azimuthalaverage_radial_TCH3OH_massivecores.png"))
pl.figure(nplots * 3 + 10).savefig(
data = np.linspace(0.01, 0.7)
fig = pl.figure(1)
pl.clf()
ax = fig.gca()
ax.plot(data, ratio_to_temperature(data), alpha=0.5, linewidth=2,
label='LTE',
)
ax.set_ylim(0,150)
ax.set_xlabel("Ratio H$_2$CO $3_{2,1}-2_{2,0} / 3_{0,3}-2_{0,2}$")
ax.set_ylabel("Temperature (K)")
fig.savefig(paths.fpath('h2co_temperature_vs_ratio_lte.png'))
pm = paraH2COmodel()
densind = np.argmin(np.abs(pm.densityarr[0,:,0]-4.5))
colind = np.argmin(np.abs(pm.columnarr[0,:,0]-13.5))
ax.plot(pm.modelratio1[:,densind,colind],
pm.temparr[:,densind,colind],
'r',
alpha=0.5,
linewidth=2,
label='$n=10^{4.5}$ cm$^{-3}$, $N=10^{13.5}$ cm$^{-2}$',
)
pl.legend(loc='best')
cube = SpectralCube.read(dpath(cube_fn)).with_spectral_unit(u.km/u.s,
velocity_convention='radio')
errspec = cube.std(axis=(1,2))
pix_area = np.abs(np.product(np.diag(cube.wcs.celestial.pixel_scale_matrix)))
beam = radio_beam.Beam.from_fits_header(cube.header)
ppbeam = beam.sr.to(u.deg**2).value/pix_area
JyToK = u.Jy.to(u.K, equivalencies=u.brightness_temperature(beam,
cube.wcs.wcs.restfrq*u.Hz))
for regname, outdir in [('W51_22_emission.reg', 'emission'),
('W51_e_apertures.reg', 'hiiregionh2co')]:
outpath = dpath('spectra/{0}'.format(outdir))
figpath = fpath('spectra/{0}'.format(outdir))
if not os.path.exists(outpath):
os.makedirs(outpath)
if not os.path.exists(figpath):
os.makedirs(figpath)
regions = pyregion.open(rpath(regname))
prefix = os.path.basename(cube_fn)
t0 = time.time()
for R in regions:
linestyle=linestyles[temperature], alpha=0.5, linewidth=3,
label="NH$_3$ 1-1 $T={0}$K".format(temperature))
ax.plot(densities, tex_nh322[temperature], color='m',
linestyle=linestyles[temperature], alpha=0.5, linewidth=3,
label="NH$_3$ 2-2 $T={0}$K".format(temperature))
ax.plot(densities, tex_h2co[temperature], color='r',
linestyle=linestyles[temperature], alpha=0.5, linewidth=3,
label="p-H$_2$CO $3_{{0,3}}-2_{{0,2}} T={0}$K".format(temperature))
ax.plot(densities, tex_h2co321[temperature], color='g',
linestyle=linestyles[temperature], alpha=0.5, linewidth=3,
label="p-H$_2$CO $3_{{2,1}}-2_{{2,0}} T={0}$K".format(temperature))
ax.vlines(1e3,0,55,color='k', alpha=0.5, linewidth=2, zorder=-10,
linestyle='--')
ax.vlines(1e4,0,55,color='k', alpha=0.5, linewidth=2, zorder=-10,
linestyle=':')
ax.vlines(1e5,0,55,color='k', alpha=0.5, linewidth=2, zorder=-10,
linestyle='-')
ax.set_xscale('log')
ax1,ax2 = fig1.axes
ax1.set_ylim(2.5,50)
ax2.set_ylim(2.5,5)
ax2.set_xlabel("Volume Density ($n(H_2)$ cm$^{-3}$)")
fig1.text(0.05, 0.5, "Excitation Temperature $T_{ex}$ (K)", rotation=90,
ha='center', va='center')
fig1.subplots_adjust(hspace=0)
ax2.legend(loc='lower right', fontsize=14)
fig1.savefig(paths.fpath("radex/NH3andH2COexcitation.pdf"))
pl.draw()
pl.show()
linewidths=0.9,
transform=ax.get_transform(
wcs.WCS(cont_b3[0].header).celestial[2700:-2700, 2700:-2700]))
ax.axis((525, 725, 375, 625))
ax.contour(hcnv1j1.value,
colors=['lime'] * 10,
levels=[100, 150],
linewidths=0.9,
transform=ax.get_transform(hcnv1j1.wcs))
ax.contour(hcnv3j1.value,
colors=['r'] * 10,
levels=[150, 200, 250, 300, 450, 500],
linewidths=0.9,
transform=ax.get_transform(hcnv3j1.wcs))
ax.contour(siov2j2.value,
colors=['c'] * 10,
levels=np.linspace(500, 10000, 5),
linewidths=0.9,
transform=ax.get_transform(siov2j2.wcs))
#ax.plot(v2maser.center.ra, v2maser.center.dec, marker='x', color='w',
# markersize=7,
# markeredgewidth=0.5,
# transform=ax.get_transform('icrs'))
ax.set_xlabel("RA (ICRS)")
ax.set_ylabel("Dec (ICRS)")
fig.savefig(paths.fpath('sgrb2n_sio_maser_and_hcn.pdf'), bbox_inches='tight')
pl.figure(ii)
pl.clf()
figs = [FITSFigure(fn, figure=pl.figure(ii+1))
for ii,fn in enumerate(files)]
for fig,fn in zip(figs,files):
if '11' in fn:
cblabel = (r'$\tau_{1-1}$')
elif '22' in fn:
cblabel = (r'$\tau_{2-2}$')
elif 'ratio' in fn:
cblabel = (r'$\tau_{1-1} / \tau_{2-2}$')
else:
raise ValueError("This is not a file: {0}".format(fn))
fig.colorbar.set_axis_label_text(cblabel)
fig.colorbar.set_axis_label_rotation(270)
fig.colorbar.set_axis_label_pad(30)
figs[2].show_colorscale(cmap=pl.cm.gist_stern, vmin=0, vmax=15)
figs[5].show_colorscale(cmap=pl.cm.gist_stern, vmin=0, vmax=15)
figs[0].save(fpath(label+'peak_observed_opticaldepth_11.pdf'), dpi=72)
figs[1].save(fpath(label+'peak_observed_opticaldepth_22.pdf'), dpi=72)
figs[2].save(fpath(label+'peak_observed_opticaldepth_ratio.pdf'), dpi=72)
figs[5].save(fpath(label+'peak_observed_opticaldepth_ratio_tex.pdf'), dpi=72)
pl.show()
'north': 58 * u.km / u.s,
}
pl.figure(1).clf()
for target in snu_min:
files = glob.glob(paths.spath("*{0}*fits".format(target)))
if len(files) == 0:
print("No matches for {0}".format(target))
continue
spectra = pyspeckit.Spectra(files)
for ii in range(4):
spectra[ii].plotter(figure=pl.figure(1))
species_names = [x[0] for x in line_to_image_list]
frequencies = u.Quantity(
[float(x[1].strip("GHz")) for x in line_to_image_list], unit=u.GHz)
spectra[ii].plotter.axis.set_ylim(
snu_min[target], spectra[ii].plotter.axis.get_ylim()[1])
spectra[ii].plotter.line_ids(species_names,
u.Quantity(frequencies),
velocity_offset=velo[target],
plot_kwargs=plot_kwargs,
annotate_kwargs=annotate_kwargs)
outname = paths.fpath('line_id_spectra/{target}_spw{0}.png'.format(
ii, target=target))
spectra[ii].plotter.savefig(outname, bbox_extra_artists=[])
for name, center, size in (('e2e', e2e, 7.5*u.arcsec),
('between_e2e_and_e8', between_e2e_and_e8, 15*u.arcsec),
('e8fil', e8fil, 12.5*u.arcsec),
('north', north, 7.5*u.arcsec)):
cutout_red = Cutout2D(red_fits[0].data, center, size, wcs=red_wcs)
cutout_blue = Cutout2D(blue_fits[0].data, center, size, wcs=blue_wcs)
cutout_m0 = Cutout2D(m0.value, center, size, wcs=cube.wcs.celestial)
fig1 = pl.figure(1)
fig1.clf()
cutout_m0_hdu = fits.PrimaryHDU(data=cutout_m0.data, header=cutout_m0.wcs.to_header())
FF = aplpy.FITSFigure(cutout_m0_hdu, figure=fig1)
FF.show_grayscale()
fig1.savefig(paths.fpath("methanol_{0}.png".format(name)))
levels = [0.5,1,2,3,4,5,6,7,8,9,10,15]
alphas = np.linspace(0.1, 0.9, len(levels))
FF.add_scalebar((0.1*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
FF.scalebar.set_label('0.1 pc')
FF.scalebar.set_color('w')
FF.show_contour(fits.PrimaryHDU(data=cutout_blue.data, header=cutout_blue.wcs.to_header()),
levels=levels,
filled=True,
colors=[(0,0,1,ii) for ii in alphas])
FF.show_contour(fits.PrimaryHDU(data=cutout_red.data, header=cutout_red.wcs.to_header()),
filled=True,
levels=levels,
vmin_r=-0.0025,
vmin_b=-0.0025,
vmin_g=0.0001,
stretch_g='linear', embed_avm_tags=True)
pl.rcParams['font.size'] = 18
fig1 = pl.figure(1)
fig1.clf()
F = aplpy.FITSFigure(rgb_cube_png, figure=fig1)
F.show_rgb(rgb_cube_png)
F.recenter(290.93315, 14.509584, radius=0.0004)
F.add_scalebar((0.025*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
F.scalebar.set_label('5000 au / 0.025 pc')
F.scalebar.set_color('w')
F.save(paths.fpath("outflows/W51e2_cycle3green_SiO_outflows_aplpy.png"))
F.save(paths.fpath("outflows/W51e2_cycle3green_SiO_outflows_aplpy.pdf"))
F.recenter(290.93318, 14.509591, radius=0.0001)
F.scalebar.set_length((0.005*u.pc / (5400*u.pc)).to(u.deg,u.dimensionless_angles()))
F.scalebar.set_label('1000 au / 0.005 pc')
F.scalebar.set_color('w')
F.save(paths.fpath("outflows/W51e2_cycle3green_SiO_outflows_aplpy_zoom.png"))
F.save(paths.fpath("outflows/W51e2_cycle3green_SiO_outflows_aplpy_zoom.pdf"))
F.show_contour(e2_green_fits, levels=[0.0007], colors=['w']*10)
F.save(paths.fpath("outflows/W51e2_cycle3green_SiO_outflows_aplpy_zoom_continuumcontours.png"))
F.save(paths.fpath("outflows/W51e2_cycle3green_SiO_outflows_aplpy_zoom_continuumcontours.pdf"))
def multiscale_fit(pcube, centerx, centery, offset_scale=0.3, savedir=None,
savepre=None, exclude=(236,323, 539,654, 972,1049,
1180,1238),
radii=(2,3,4,5,6,7,8,9),
guesses=[0.84,37.9,6.5,0.5,0.8,1.0]):
pcube.plotter.figure = pl.figure(1)
pcube.plotter.axis = pl.figure(1).gca()
pcube.specfit.Registry.add_fitter('h2co_simple', simple_fitter2, 6,
multisingle='multi')
sp = pcube.get_spectrum(centerx, centery)
sp.plotter.figure = pl.figure(1)
sp.plotter.axis = pl.figure(1).gca()
fitspec(sp, exclude, guesses)
sp.specfit.plotresiduals(axis=pcube.plotter.axis,
yoffset=-sp.specfit.parinfo[0].value*offset_scale,
clear=False, color='#444444', label=False)
sp.plotter.axis.set_ylim(-5*sp.specfit.residuals.std()
-sp.specfit.parinfo[0].value*offset_scale,
pcube.plotter.axis.get_ylim()[1])
sp.plotter.savefig(paths.fpath('{0}/{1}_r0.pdf'.format(savedir, savepre)))
r1b = []
er1b = []
sigb = []
esigb = []
r1 = [sp.specfit.parinfo[3].value]
er1 = [sp.specfit.parinfo[3].error]
sig = [sp.specfit.parinfo[2].value]
esig = [sp.specfit.parinfo[2].error]
cen = [sp.specfit.parinfo[1].value]
ecen = [sp.specfit.parinfo[1].error]
mergefig = pl.figure(0)
mergefig.clf()
splast = sp
for ii,radius in enumerate(radii):
sp = pcube.get_apspec([centerx, centery, radius], wunit='pixel')
sp.plotter(figure=pl.figure(1))
fitspec(sp, exclude, guesses)
sp.specfit.plotresiduals(axis=pcube.plotter.axis,
yoffset=-sp.specfit.parinfo[0].value*offset_scale,
clear=False, color='#444444', label=False)
sp.plotter.axis.set_ylim(-5*sp.specfit.residuals.std()
-sp.specfit.parinfo[0].value*offset_scale,
sp.plotter.axis.get_ylim()[1])
sp.plotter.savefig(paths.fpath('{0}/{1}_r{2}.pdf'.format(savedir,
savepre,
radius)))
r1.append(sp.specfit.parinfo[3].value)
er1.append(sp.specfit.parinfo[3].error)
sig.append(sp.specfit.parinfo[2].value)
esig.append(sp.specfit.parinfo[2].error)
cen.append(sp.specfit.parinfo[1].value)
ecen.append(sp.specfit.parinfo[1].error)
spannulus = sp - splast
spannulus.plotter.figure = sp.plotter.figure
spannulus.plotter.axis = sp.plotter.axis
fitspec(spannulus, exclude, guesses=sp.specfit.parinfo.values)
spannulus.specfit.plotresiduals(axis=pcube.plotter.axis,
yoffset=-spannulus.specfit.parinfo[0].value*offset_scale,
clear=False, color='#444444', label=False)
spannulus.plotter.axis.set_ylim(-5*spannulus.specfit.residuals.std()
-spannulus.specfit.parinfo[0].value*offset_scale,
spannulus.plotter.axis.get_ylim()[1])
spannulus.plotter.savefig(paths.fpath('{0}/{1}_r{2}_annulus.pdf'.format(savedir,
savepre,
radius)))
r1b.append(spannulus.specfit.parinfo[3].value)
er1b.append(spannulus.specfit.parinfo[3].error)
sigb.append(spannulus.specfit.parinfo[2].value)
esigb.append(spannulus.specfit.parinfo[2].error)
splast = sp.copy()
sp.smooth(4)
sp.plotter(figure=mergefig, clear=False,
axis=mergefig.gca(),
color=pl.cm.spectral(ii/float(len(radii))),
label="{0:0.1f}".format(radius*7.2))
sp.plotter.axis.relim()
sp.plotter.axis.autoscale()
pl.figure(mergefig.number)
pl.legend(loc='upper right', fontsize=16)
mergefig.savefig(paths.fpath('{0}/{1}_mergefig.pdf'.format(savedir,
savepre)))
pl.figure(2)
pl.clf()
ax1 = pl.subplot(2,1,1)
ax1.errorbar(np.arange(1,10)*7.2, r1, yerr=er1, linestyle='none',
marker='s', color='k')
ax1.set_xlim(0.5*7.2,9.5*7.2)
#ax1.set_xlabel("Aperture Radius (arcseconds)")
ax1.set_ylabel("Ratio $3_{2,1}-2_{2,0} / 3_{0,3} - 2_{0,2}$")
ax1.xaxis.set_ticklabels([])
# Remove the bottom label (overlap)
tl = ax1.yaxis.get_ticklabels()
tl[0].set_visible(False)
ax1b = ax1.twinx()
# set the y limits to pwtem(the ratio limits)
ylim = ax1.get_ylim()
# pwtem can't go above 0.6; returns NaN above that
if ylim[1] > 0.599999:
ylim = (ylim[0], 0.599999)
ax1.set_ylim(*ylim)
ax1b.set_ylim(*pwtem(ylim))
ax1b.xaxis.set_ticklabels([])
ax1b.set_ylabel('Temperature (K)')
ax2 = pl.subplot(2,1,2)
pl.subplots_adjust(hspace=0)
ax2.errorbar(np.arange(1,10)*7.2, sig, yerr=esig, linestyle='none',
marker='s', color='k')
ax2.set_xlim(0.5*7.2,9.5*7.2)
ax2.set_xlabel("Aperture Radius (arcseconds)")
ax2.set_ylabel("$\sigma$ (km s$^{-1}$)")
# Steve suggested adding a Mach number label, but this isn't possible:
# it would require a 3rd graph, since M ~ sigma/sqrt(T)
# ax2b = ax2.twinx()
# # set the y limits to the sigma limits
# ylim = ax2.get_ylim()
# ax2b.set_ylim()
# ax2b.xaxis.set_ticklabels([])
# ax2b.set_ylabel('Temperature (K)')
pl.savefig(paths.fpath('{0}/{1}_ratio_vs_scale.pdf'.format(savedir, savepre)))
ax1.errorbar(np.arange(2,10)*7.2, r1b, yerr=er1b, linestyle='none',
marker='s', color='b', zorder=-1, alpha=0.5)
ax2.errorbar(np.arange(2,10)*7.2, sigb, yerr=esigb, linestyle='none',
marker='s', color='b', zorder=-1, alpha=0.5)
pl.savefig(paths.fpath('{0}/{1}_ratio_vs_scale_annuli.pdf'.format(savedir, savepre)))
return r1,er1,sig,esig,cen,ecen,r1b,er1b,sigb,esigb
err=noise_norm,
return_error=True)
for v in ProgressBar(np.arange(vr[0],vr[1]+dv.value,dv.value))]
integrated_image = (scube.spectral_slab(vr[0]*u.km/u.s,
vr[1]*u.km/u.s).moment0(axis=0))
gaussfit_total,fitimage = gaussfitter.gaussfit(np.nan_to_num(integrated_image.value),
err=noise.value,
returnfitimage=True
)
# sanity check: make sure the fit is OK (it is)
pl.figure(2).clf()
pl.imshow(integrated_image.value, cmap=pl.cm.bone_r)
cb = pl.colorbar()
pl.contour(fitimage, cmap=pl.cm.spectral)
pl.savefig(fpath('sanitycheck_w51e8_core_gaussfits_{0}.png'.format(name)))
pl.figure(3).clf()
pl.imshow((u.Quantity(integrated_image)/(u.km/u.s))
.to(u.K, u.brightness_temperature(beam, 14.488*u.GHz)).value,
cmap=pl.cm.bone_r)
cb = pl.colorbar()
pl.contour(fitimage, cmap=pl.cm.spectral)
pl.savefig(fpath('sanitycheck_K_w51e8_core_gaussfits_{0}.png'.format(name)))
print(name)
print("Integrated gaussfit: ", gaussfit_total)
centerx, centery = gaussfit_total[2:4]
celwcs = scube.wcs.sub([wcs.WCSSUB_CELESTIAL])
cx_w, cy_w = celwcs.wcs_pix2world([[centerx, centery]], 0)[0]
center_coord = coordinates.SkyCoord(cx_w, cy_w, unit=('deg','deg'), frame='fk5')
#F.show_rgb('/Volumes/128gbdisk/w51/pngs/W51_4096sq_WISE_bolo_mosaic_rotated_blackbg.png',wcs=hwcs)
F.show_rgb(paths.pdpath('W51_4096sq_WISE_bolo_mosaic_rotated_blackbg.png'),
wcs=hwcs)
#F.show_regions(paths.rpath('large_scale_regions.reg'))
regions = pyregion.open(paths.rpath('large_scale_regions.reg'))
#text = re.compile("text={([^}]*)}")
for reg in regions:
#t = text.search(reg.comment).groups()[0]
t = reg.attr[1]['text']
F.add_label(reg.coord_list[0], reg.coord_list[1], t, color='white',
size=16, weight='bold')
F.set_tick_labels_xformat('dd.d')
F.set_tick_labels_yformat('dd.d')
F.recenter(49.27, -0.32, width=0.9, height=0.4)
F.save(paths.fpath('W51_wisecolor_largescale_labeled.pdf'), dpi=72)
F.show_rgb(paths.dpath("make_pretty_picture/W51_modified.png",paths.datapath_w51),
wcs=hwcs)
F.save(paths.fpath('W51_wisecolor_modified_largescale_labeled.pdf'), dpi=72)
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_font_size(18)
F.scalebar.set_font_weight('bold')
F.scalebar.set_color('w')
F.scalebar.set_linewidth(3)
F.save(paths.fpath('W51_wisecolor_modified_largescale_labeled_scalebar.pdf'), dpi=150)
F.scalebar.hide()
for L in list(F._layers.keys()):
if L in F._layers:
.format(cl_masses_flat[n_ostars == 12].mean(),
cl_masses_flat[n_ostars == 12].std()))
print(
"Implied stellar mass of e1e2IRS2 cluster, assuming 18 stars are O stars: {0} +/- {1} M_sun"
.format(cl_masses_flat[n_ostars == 18].mean(),
cl_masses_flat[n_ostars == 18].std()))
pl.figure(6).clf()
pl.semilogx(cl_masses_flat, luminosities, '.', alpha=0.5)
pl.axhline(np.log10(8.3e6), linewidth=10, alpha=0.1, zorder=-5)
pl.axhline(np.log10(2e7), linewidth=10, alpha=0.1, zorder=-5)
pl.xlim(10**2.5, (maxmass))
pl.ylim(5.5, 8.1)
pl.xlabel("Cluster mass ($M_\odot$)")
pl.ylabel("Cluster luminosity (log $L_\odot$)")
pl.savefig(paths.fpath("clusters/cluster_mass_vs_luminosity.png"))
pl.figure(9).clf()
pl.loglog(cl_masses_flat,
n_ostars,
'.',
alpha=0.5,
label='O-stars $(M>{0} M_\odot)$'.format(mmin_ostar))
pl.loglog(cl_masses_flat,
n_obstars,
'.',
alpha=0.5,
label='B-stars $(M>{0} M_\odot)$'.format(mmin_bstar))
pl.xlim(10**2.5, (maxmass))
pl.ylim(0, n_ostars.max())
pl.ylim(0, n_obstars.max())
from astropy import log
import paths
from spectral_cube import SpectralCube, BooleanArrayMask
import matplotlib
matplotlib.rc_file(paths.pcpath('pubfiguresrc'))
from temperature_cubes import (tcube_dend, tcube_dend_smooth, tcube_direct,
tcubesm_direct)
from ratio_cubes import (ratiocube_303321, ratiocubesm_303321)
from masked_cubes import (cube303m, cube303msm, cube303, cube303sm)
from astropy import units as u
from image_registration.fft_tools import downsample
for ii in range(1,5):
pl.close(ii)
if not os.path.isdir(paths.fpath('temvslon')):
os.mkdir(paths.fpath('temvslon'))
figsize=(20,10)
vmin,vmax = -145.,135.
cbvmin,cbvmax = -80, 120
dv = 20.
vranges = np.arange(vmin, vmax, dv)
cm = pl.cm.rainbow_r
segmentdata = {'alpha': [(0.0, 1.0, 1.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0)],
'blue': [(0.0, 1.0, 1.0), (0.5, 0.0, 0.0), (1.0, 0.0, 0.0)],
'green': [(0.0, 0.0, 0.0), (0.5, 0.75, 0.75), (1.0, 0.0, 0.0)],
'red': [(0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0)]}
cm = matplotlib.colors.LinearSegmentedColormap(name='rgb',
segmentdata=segmentdata)
m1.hdu.writeto(m1fn, clobber=True)
m2.quicklook()
m2.hdu.writeto(m2fn, clobber=True)
pmax.quicklook()
pmax.hdu.writeto(maxfn, clobber=True)
madstd.quicklook()
madstd.hdu.writeto(madstdfn, clobber=True)
# argmax.quicklook()
# argmax.hdu.writeto(argmaxfn, clobber=True)
try:
m0.FITSFigure.save(fpath("moments/{0}_moment0.png".format(fname)))
m1.FITSFigure.show_colorscale(cmap='viridis', vmin=45, vmax=68)
m1.FITSFigure.show_contour(m0.hdu, levels=[4], colors=['k'])
m1.FITSFigure.save(fpath("moments/{0}_moment1.png".format(fname)))
m2.FITSFigure.show_colorscale(cmap='viridis', vmin=0, vmax=30)
m2.FITSFigure.show_contour(m0.hdu, levels=[4], colors=['k'])
m2.FITSFigure.save(fpath("moments/{0}_moment2.png".format(fname)))
pmax.FITSFigure.show_colorscale(cmap='viridis')
pmax.FITSFigure.show_contour(m0.hdu, levels=[4], colors=['k'])
pmax.FITSFigure.save(fpath("moments/{0}_max.png".format(fname)))
# argmax.FITSFigure.show_colorscale(cmap='RdYlBu_r', vmin=45, vmax=68)
# argmax.FITSFigure.save(fpath("moments/{0}_argmax.png".format(fname)))
for ra, dec, rad, name in ((290.93253, 14.508016, 0.00311407, 'e2e8'),
(290.9118, 14.512366, 0.00311407,
'southeast'), (290.9166, 14.518094,
if __name__ == "__main__":
import matplotlib
matplotlib.rc_file(paths.pcpath('pubfiguresrc'))
densities = np.logspace(3,7,20)
tem = [tkin_all(n*u.cm**-3, 10*u.km/u.s, lengthscale=5*u.pc,
gradient=5*u.km/u.s/u.pc, tdust=25*u.K,
crir=1e-17*u.s**-1) for n in ProgressBar(densities)]
pl.figure(1)
pl.clf()
pl.plot(densities, tem, 'k--', label='CRIR=1e-17, $\sigma=10$ km/s')
pl.xlabel(r'$\log\,N_{\rm H}$')
pl.ylabel('Temperature (K)')
pl.xscale('log')
pl.legend(loc='best')
pl.savefig(paths.fpath("despotic/TvsN.png"))
linewidths = np.arange(0.5,30,2)
linewidths = np.logspace(np.log10(0.5), np.log10(30), 15)
fiducial = tem2 = [tkin_all(1e4*u.cm**-3, sigma*u.km/u.s, lengthscale=5*u.pc,
gradient=5*u.km/u.s/u.pc, tdust=25*u.K,
tdust_rad=10*u.K,
crir=1e-17*u.s**-1) for sigma in ProgressBar(linewidths)]
tem3 = [tkin_all(1e5*u.cm**-3, sigma*u.km/u.s, lengthscale=5*u.pc,
gradient=5*u.km/u.s/u.pc, tdust=25*u.K,
tdust_rad=10*u.K,
crir=1e-17*u.s**-1) for sigma in ProgressBar(linewidths)]
tem4 = [tkin_all(1e5*u.cm**-3, sigma*u.km/u.s, lengthscale=5*u.pc,
gradient=5*u.km/u.s/u.pc, tdust=25*u.K,
tdust_rad=10*u.K,
pl.clf()
ax = fig.gca()
ax.plot(
data,
ratio_to_temperature(data),
alpha=0.5,
linewidth=2,
label='LTE',
)
ax.set_ylim(0, 150)
ax.set_xlabel("Ratio H$_2$CO $3_{2,1}-2_{2,0} / 3_{0,3}-2_{0,2}$")
ax.set_ylabel("Temperature (K)")
fig.savefig(paths.fpath('h2co_temperature_vs_ratio_lte.png'))
pm = paraH2COmodel()
densind = np.argmin(np.abs(pm.densityarr[0, :, 0] - 4.5))
colind = np.argmin(np.abs(pm.columnarr[0, :, 0] - 13.5))
ax.plot(
pm.modelratio1[:, densind, colind],
pm.temparr[:, densind, colind],
'r',
alpha=0.5,
linewidth=2,
label='$n=10^{4.5}$ cm$^{-3}$, $N=10^{13.5}$ cm$^{-2}$',
)
velocity=row['velocity']*u.km/u.s,
)
spectra = pyspeckit.Spectra(speclist)
spectra.xarr.convert_to_unit(u.GHz)
mod = methanolmodel_osu.lte_model(spectra.xarr,
row['velocity']*u.km/u.s, 5*u.km/u.s,
300*u.K, 5e17*u.cm**-2)
median = np.nanmedian(spectra.data)
for ii in range(1,8):
pl.subplot(7,1,ii)
pl.plot(spectra.xarr, mod+median, color='r', linewidth=1, alpha=0.5)
pl.savefig(paths.fpath(figname[:-4]+"_model.png"), dpi=300,
bbox_inches='tight', bbox_extra_artists=[])
# Acetone lines (for identification purposes)
from astroquery.splatalogue import Splatalogue
Splatalogue.LINES_LIMIT=5000
acetone_lines = Splatalogue.query_lines(218*u.GHz, 235*u.GHz,
energy_max=800,
energy_type='eu_k',
chemical_name='Acetone')
from generic_lte_molecule_model import LTEModel
acetonemodel = LTEModel(chemical_name='Acetone')
for row in myvtbl:
ax3 = fig2.add_subplot(212)
with contextlib.redirect_stderr(devnull):
fit = powerlaw.Fit(core_phot_tbl['peak'])
# doesn't work at all fit.plot_pdf(color='k')
ax3.hist(core_phot_tbl['peak'],
bins=np.logspace(-3, -0.5, 15),
color='k',
facecolor='none',
histtype='step')
ax3.set_xscale('log')
fit.power_law.plot_pdf(color='r', linestyle='--')
ax3.set_ylim(0.3, 15)
ax3.set_xlabel("Peak flux density (Jy/beam)")
ax3.set_ylabel("Number of sources")
fig2.savefig(paths.fpath('coreplots/flux_powerlaw_histogram_fit.png'))
print("Flux Fit parameters: alpha={0}".format(fit.power_law.alpha))
fig2 = pl.figure(2)
fig2.clf()
ax2 = fig2.add_subplot(211)
print()
print("Core distribution: tem-corrected peak mass")
with contextlib.redirect_stderr(devnull):
fit = powerlaw.Fit(cores_merge['T_corrected_peakmass'])
fit.plot_ccdf(color='k')
fit.power_law.plot_ccdf(color='r', linestyle='--')
ax2.set_ylabel("Fraction of sources")
for species_tuple in species_list:
species_name, chemid, tmax = species_tuple
for ii in range(4):
cat = Splatalogue.query_lines(
spectra[ii].xarr.min(),
spectra[ii].xarr.max(),
chemical_name=chemid,
energy_max=tmax,
energy_type="eu_k",
noHFS=True,
line_lists=["SLAIM"],
)
spectra[ii].plotter(figure=pl.figure(1))
spectra[ii].plotter.axis.set_ylim(snu_min[target], spectra[ii].plotter.axis.get_ylim()[1])
spectra[ii].plotter.line_ids(
cat["Resolved QNs"],
cat["Freq-GHz"] * u.GHz,
velocity_offset=velo[target],
plot_kwargs=plot_kwargs,
annotate_kwargs=annotate_kwargs,
)
spectra[ii].plotter.savefig(
paths.fpath(
"line_id_spectra/{target}_{species_name}_{chemid}_spw{0}.png".format(
ii, target=target, chemid=chemid, species_name=species_name
)
),
bbox_extra_artists=[],
)
"""
Quick-look to demonstrate that the off positions are relatively emission-free
"""
import aplpy
from astropy.utils.data import download_file
import paths
dame2001 = download_file('http://www.cfa.harvard.edu/mmw/Wco_DHT2001.fits.gz',
cache=True)
F = aplpy.FITSFigure(dame2001, convention='calabretta')
F.show_grayscale()
F.recenter(0, 0, width=5, height=3) # center on the inner 5x3 degrees
F.show_regions(paths.rpath('target_fields_8x8.reg'))
F.show_regions(paths.rpath('off_positions_selectedfromDame2001.reg'))
F.save(paths.fpath('Dame2001_APEXCMZ_offpositions.png'))
F.save(paths.fpath('Dame2001_APEXCMZ_offpositions.pdf'))
F.hide_layer('region_set_1')
F.hide_layer('region_set_1_txt')
F.show_regions(paths.rpath('target_fields_8x8_coloredbyoffposition.reg'))
F.save(paths.fpath('Dame2001_APEXCMZ_offpositions_coloredbyoff.png'))
img = locals()[name].value
hdu = fits.PrimaryHDU(data=img, header=h112h)
pl.figure(ii)
pl.clf()
F = aplpy.FITSFigure(hdu, figure=pl.figure(ii))
vmax = [vmaxes[k] for k in vmaxes if k in name][0]
vmin = [vmins[k] for k in vmins if k in name][0]
F.show_colorscale(vmin=vmin, vmax=vmax, cmap=cmap)
F.recenter(49.235, -0.303, width=0.952, height=0.433)
F.add_colorbar()
F._ax1.set_title(titles[name])
F.set_tick_labels_xformat("dd.d")
F.set_tick_labels_yformat("dd.d")
# F.set_tick_xspacing(0.3)
figs.append(F)
F.save(fpath("ratiomap_cont_%s.pdf" % name))
F.save(fpath("ratiomap_cont_%s.png" % name))
F.show_contour(
he77a_name, convention="calabretta", levels=[0.0125, 0.025, 0.05, 0.1, 0.15, 0.2], colors="w", linewidth=0.5
)
F.save(fpath("ratiomap_cont_%s_HeContours.pdf" % name))
pl.figure(ii + 1)
pl.clf()
F = aplpy.FITSFigure(h77cname, figure=pl.figure(ii + 1), convention="calabretta")
F.show_grayscale(vmin=-0.1, vmid=-0.4, vmax=550, invert=True, stretch="log")
F.recenter(49.235, -0.303, width=0.952, height=0.433)
F.show_contour(hdu, levels=[0.3, 0.5, 0.7], colors=["b", (0.2, 1, 0.4, 0.8), "r"])
F.add_colorbar()
pl.figure(ii + 2)
sp.specfit.Registry.add_fitter('h2co_simple', simple_fitter2, 6,
multisingle='multi')
sp.baseline(exclude=[151, 761, 916, 1265], selectregion=True,
highlight_fitregion=True, xtype='pixel', subtract=True)
sp.error[:] = sp.data[761:916].std()
sp.plotter()
sp.specfit(fittype='h2co_simple',
guesses=[0.06, 10, 20, 0.5, 0.7, 0.03],
limited=[(True,True)] * 6,
limits=[(0,20),[-150,150],(1, 60),(0,1),(0.3,1.1),(0,1e5)],
)
sp.specfit.plot_fit(show_components=True)
sp.plotter.savefig(paths.fpath('simple/WholeCMZ_6parameter.pdf'),
bbox_inches='tight')
lat,lon = full_cubes.cube_merge_high.world[0,:,:][1:]
sgrb2_cloud_mask = ((lon-0.674*u.deg)**2 + (lat+0.027*u.deg)**2)**0.5 < 5*u.arcmin
spd_nob2 = full_cubes.cube_merge_high.with_mask(~sgrb2_cloud_mask).mean(axis=(1,2)).value
sp2 = pyspeckit.Spectrum(xarr=full_cubes.cube_merge_high.spectral_axis,
data=spd_nob2,
xarrkwargs={'velocity_convention': 'radio'},
)
sp2.xarr.refX = full_cubes.pcube_merge_high.xarr.refX
sp2.xarr.refX_unit = full_cubes.pcube_merge_high.xarr.refX_unit
sp2.xarr.convert_to_unit(u.GHz)
sp2.specname = 'Whole CMZ'
sp2.unit = 'K'
