def fit_and_save(regname, fignum=1, pfignum=2):
pc = do_1comp_region(regname, **regions[regname])
pc.mapplot.figure = pl.figure(fignum)
pc.plotter.figure = pl.figure(pfignum)
pc.mapplot(estimator=3, cmap=cm)
plane = pc.mapplot.plane
plane[plane == 0.0] = np.nan
temmap = piecewise_rtotem.pwtem(plane.flat).reshape(plane.shape)
hdu = fits.PrimaryHDU(data=temmap, header=pc.mapplot.header)
hdu.writeto(hpath("{0}_pyspeckit_tmap.fits".format(regname)), clobber=True)
pc.mapplot.figure.clf()
FF = aplpy.FITSFigure(hdu,
figure=pc.mapplot.figure,
convention='calabretta')
FF.set_tick_labels_format('d.dd', 'd.dd')
FF.show_colorscale(cmap=cm, vmin=15, vmax=200, interpolation='nearest')
FF.add_colorbar()
FF.save(fpath("{0}_pyspeckit_tmap.pdf".format(regname)), dpi=150)
#pl.savefig(fpath("{0}_pyspeckit_tmap.pdf".format(regname)))
return pc
def fit_and_save(regname, fignum=1, pfignum=2):
pc = do_1comp_region(regname, **regions[regname])
pc.mapplot.figure = pl.figure(fignum)
pc.plotter.figure = pl.figure(pfignum)
pc.mapplot(estimator=3, cmap=cm)
plane = pc.mapplot.plane
plane[plane == 0.0] = np.nan
temmap = piecewise_rtotem.pwtem(plane.flat).reshape(plane.shape)
hdu = fits.PrimaryHDU(data=temmap,
header=pc.mapplot.header)
hdu.writeto(hpath("{0}_pyspeckit_tmap.fits".format(regname)),
clobber=True)
pc.mapplot.figure.clf()
FF = aplpy.FITSFigure(hdu, figure=pc.mapplot.figure,
convention='calabretta')
FF.set_tick_labels_format('d.dd','d.dd')
FF.show_colorscale(cmap=cm, vmin=15, vmax=200, interpolation='nearest')
FF.add_colorbar()
FF.save(fpath("{0}_pyspeckit_tmap.pdf".format(regname)), dpi=150)
#pl.savefig(fpath("{0}_pyspeckit_tmap.pdf".format(regname)))
return pc
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
F.add_label(1.60,
-0.22,
"$v=[{0}, {1}]$ km s$^{{-1}}$".format(
vrange[0], vrange[1]),
color='w',
size=14,
zorder=20,
horizontalalignment='left')
F.save(
os.path.join(
figurepath, 'big_maps',
"big_H2CO_321220_to_303202{0}_bl_{1}to{2}_slabratio.pdf".
format(smooth, int(vrange[0]), int(vrange[1]))))
tproj = np.copy(proj)
tproj[np.isfinite(proj)] = pwtem(proj[np.isfinite(proj)].value)
hdu = fits.PrimaryHDU(tproj, projhi.hdu.header)
fig.clf()
F = aplpy.FITSFigure(hdu, convention='calabretta', figure=fig)
#cm = copy.copy(pl.cm.rainbow)
cm = copy.copy(pl.cm.RdYlBu_r)
cm.set_bad('#888888')
#cm.set_bad((0.5,0.5,0.5,0.5))
F.show_colorscale(cmap=cm, vmin=15, vmax=200)
F.set_tick_labels_format('d.dd', 'd.dd')
peaksn = sn.spectral_slab(*(vrange * u.km / u.s)).max(axis=0)
peaksn[(peaksn < 0) | np.isnan(peaksn)] = 0
color = (0.5, ) * 3 # should be same as background #888
nlevs = 50
F.show_contour(peaksn.hdu,
# fashion!)
#pv = pvextractor.extract_pv_slice(cube, P, respect_nan=False)
pv = pvextractor.extract_pv_slice(cube, P, respect_nan=True)
if not os.path.isdir(os.path.dirname(pvfilename)):
os.mkdir(os.path.dirname(pvfilename))
print("writing {0}".format(pvfilename))
pv.writeto(pvfilename)
bad_cols = np.isnan(np.nanmax(pv.data, axis=0))
nandata = np.isnan(pv.data)
pv.data[nandata & ~bad_cols] = 0
ok = ~nandata & ~bad_cols
if 'TemperatureFromRatio' in molecule:
pv.data[ok] = pwtem(pv.data[ok])
fig1 = pl.figure(1, figsize=figsize)
fig1.clf()
ax = fig1.gca()
mywcs = WCS(pv.header)
xext, = mywcs.sub([1]).wcs_pix2world((0,pv.shape[1]), 0)
print("xext: ",xext)
yext, = mywcs.sub([2]).wcs_pix2world((0,pv.shape[0]), 0)
yext /= 1e3
dy = yext[1]-yext[0]
dx = xext[1]-xext[0]
#F = aplpy.FITSFigure(pv, figure=fig1)
#actual_aspect = pv.shape[0]/float(pv.shape[1])
if 'Temperature' in molecule:
#F.show_colorscale(cmap=cmap, aspect=0.5/actual_aspect, vmin=vmin, vmax=vmax)
# need to respect it and then manually flag (in a rather unreliable
# fashion!)
#pv = pvextractor.extract_pv_slice(cube, P, respect_nan=False)
pv = pvextractor.extract_pv_slice(cube, P, respect_nan=True)
if not os.path.isdir(os.path.dirname(pvfilename)):
os.mkdir(os.path.dirname(pvfilename))
pv.writeto(pvfilename)
bad_cols = np.isnan(np.nanmax(pv.data, axis=0))
nandata = np.isnan(pv.data)
pv.data[nandata & ~bad_cols] = 0
ok = ~nandata & ~bad_cols
if 'TemperatureFromRatio' in molecule:
pv.data[ok] = pwtem(pv.data[ok])
fig1 = pl.figure(1, figsize=figsize)
fig1.clf()
ax = fig1.gca()
mywcs = WCS(pv.header)
xext, = mywcs.sub([1]).wcs_pix2world((0,pv.shape[1]), 0)
print "xext: ",xext
yext, = mywcs.sub([2]).wcs_pix2world((0,pv.shape[0]), 0)
yext /= 1e3
dy = yext[1]-yext[0]
dx = xext[1]-xext[0]
#F = aplpy.FITSFigure(pv, figure=fig1)
#actual_aspect = pv.shape[0]/float(pv.shape[1])
if 'Temperature' in molecule:
#F.show_colorscale(cmap=cmap, aspect=0.5/actual_aspect, vmin=vmin, vmax=vmax)
import numpy as np
from paths import hpath, apath
from spectral_cube import SpectralCube, BooleanArrayMask
from ratio_cubes import (ratio303321, eratio303321, noise_flat, mask, ratioOK,
ratio303321sm, ratioOKsm, masksm)
from masked_cubes import (cube303m,cube321m,cube303msm,cube321msm,
cube303,cube321,cube303sm,cube321sm,
sncube, sncubesm)
from piecewise_rtotem import pwtem
tcubedata = np.empty(cube303m.shape)
tcubedata[~mask] = np.nan
tcubedata[mask] = pwtem(ratio303321[ratioOK])
tcube = SpectralCube(data=tcubedata, wcs=cube303m.wcs,
mask=cube303m.mask, meta={'unit':'K'},
header=cube303m.header,
)
assert tcube.header['CUNIT3'] == 'km s-1'
tcube_direct = tcube
# smoothed version
tcubedatasm = np.empty(cube303msm.shape)
tcubedatasm[~masksm] = np.nan
tcubedatasm[masksm] = pwtem(ratio303321sm[ratioOKsm])
tcubesm = SpectralCube(data=tcubedatasm, wcs=cube303msm.wcs,
mask=cube303msm.mask, meta={'unit':'K'},
header=cube303msm.header,)
assert tcubesm.header['CUNIT3'] == 'km s-1'
from piecewise_rtotem import pwtem
from temperature_cubes import tcubesm_direct, tcube_direct
tcube_direct.write(hpath('TemperatureCube_PiecewiseFromRatio.fits'),
overwrite=True)
tcubesm_direct.write(hpath('TemperatureCube_smooth_PiecewiseFromRatio.fits'),
overwrite=True)
# Do the integrated version
for smooth in ('', '_smooth'):
top = hpath('APEX_H2CO_321_220{0}_bl_mask_integ.fits'.format(smooth))
bot = hpath('APEX_H2CO_303_202{0}_bl_mask_integ.fits'.format(smooth))
ff = fits.open(top)
rr = ff[0].data / fits.getdata(bot)
tem = pwtem(rr.ravel())
ff[0].data = tem.reshape(rr.shape)
ff.writeto(hpath(
'IntegratedRatioPiecewiseTemperature{0}_303to321.fits'.format(smooth)),
clobber=True)
# Try the same thing but on the dendrogrammed data. Basically, this is
# dendro_temperature but *much* faster
for sm, cubeA, cubeB, objects in zip(
(
"",
"_smooth",
),
(
cube303,
from dendrograms import dend, dendsm
from piecewise_rtotem import pwtem
from temperature_cubes import tcubesm_direct, tcube_direct
tcube_direct.write(hpath('TemperatureCube_PiecewiseFromRatio.fits'), overwrite=True)
tcubesm_direct.write(hpath('TemperatureCube_smooth_PiecewiseFromRatio.fits'), overwrite=True)
# Do the integrated version
for smooth in ('', '_smooth'):
top = hpath('APEX_H2CO_321_220{0}_bl_mask_integ.fits'.format(smooth))
bot = hpath('APEX_H2CO_303_202{0}_bl_mask_integ.fits'.format(smooth))
ff = fits.open(top)
rr = ff[0].data/fits.getdata(bot)
tem = pwtem(rr.ravel())
ff[0].data = tem.reshape(rr.shape)
ff.writeto(hpath('IntegratedRatioPiecewiseTemperature{0}_303to321.fits'.format(smooth)),
clobber=True)
# Try the same thing but on the dendrogrammed data. Basically, this is
# dendro_temperature but *much* faster
for sm,cubeA,cubeB,objects in zip(("","_smooth",),
(cube303,cube303sm,),
(cube321,cube321sm,),
(dend,dendsm,),):
# reset data
tcubedata = np.empty(cubeA.shape)
tcubedata[:] = np.nan
#F.recenter(**small_recen)
#F.save(os.path.join(figurepath, ratio.replace(".fits",".pdf")))
F.recenter(**big_recen)
F.add_label(1.60, -0.22,
"$v=[{0}, {1}]$ km s$^{{-1}}$".format(vrange[0],vrange[1]),
color='w', size=14, zorder=20,
horizontalalignment='left')
F.save(os.path.join(figurepath,
'big_maps',
"big_H2CO_321220_to_303202{0}_bl_{1}to{2}_slabratio.pdf"
.format(smooth,int(vrange[0]),int(vrange[1]))
)
)
tproj = np.copy(proj)
tproj[np.isfinite(proj)] = pwtem(proj[np.isfinite(proj)].value)
hdu = fits.PrimaryHDU(tproj, projhi.hdu.header)
fig.clf()
F = aplpy.FITSFigure(hdu,
convention='calabretta',
figure=fig)
#cm = copy.copy(pl.cm.rainbow)
cm = copy.copy(pl.cm.RdYlBu_r)
cm.set_bad('#888888')
#cm.set_bad((0.5,0.5,0.5,0.5))
F.show_colorscale(cmap=cm,vmin=15,vmax=200)
F.set_tick_labels_format('d.dd','d.dd')
peaksn = sn.spectral_slab(*(vrange*u.km/u.s)).max(axis=0)
peaksn[(peaksn<0) | np.isnan(peaksn)] = 0
color = (0.5,)*3 # should be same as background #888
import numpy as np
from paths import hpath, apath
from spectral_cube import SpectralCube, BooleanArrayMask
from ratio_cubes import (ratio303321, eratio303321, noise_flat, mask, ratioOK,
ratio303321sm, ratioOKsm, masksm)
from masked_cubes import (cube303m, cube321m, cube303msm, cube321msm, cube303,
cube321, cube303sm, cube321sm, sncube, sncubesm)
from piecewise_rtotem import pwtem
tcubedata = np.empty(cube303m.shape)
tcubedata[~mask] = np.nan
tcubedata[mask] = pwtem(ratio303321[ratioOK])
tcube = SpectralCube(
data=tcubedata,
wcs=cube303m.wcs,
mask=cube303m.mask,
meta={'unit': 'K'},
header=cube303m.header,
)
assert tcube.header['CUNIT3'] == 'km s-1'
tcube_direct = tcube
# smoothed version
tcubedatasm = np.empty(cube303msm.shape)
tcubedatasm[~masksm] = np.nan
tcubedatasm[masksm] = pwtem(ratio303321sm[ratioOKsm])
tcubesm = SpectralCube(
data=tcubedatasm,
wcs=cube303msm.wcs,
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
