def show_con(dgmf, fignum=3, label="$f(n>10^4$ cm$^{-3})$"):
figN = pl.figure(fignum)
figN.clf()
gray = copy.copy(pl.cm.gray)
gray.set_bad('black')
gray.set_under('black')
FF = FITSFigure(cube13ss_slab3_masked_mom0.hdu, figure=figN, colorbar=False,
cmap=gray, transparent_nan=False)
#FF = FITSFigure(dgmf1e4, figure=fig4)
#FF.show_grayscale()
FF.show_contour(dgmf, levels=[0.1,0.3,0.5,0.7,0.9], zorder=1000, smooth=1)
FF.add_colorbar()
cax = FF.colorbar._colorbar_axes
cax.collections[0].set_visible(False)
FF.colorbar = pl.colorbar(FF._layers['contour_set_1'], cax=cax)
FF.colorbar.set_label("Dense Fraction", rotation=270, labelpad=30)
for lines in FF.colorbar.lines:
lines.set_linewidth(76)
FF._ax1.set_title(label)
#FF.scalebar._scalebar.set_zorder(40)
#FF.refresh()
return FF
cmhot.set_under('white')
cmhot.set_over('black')
cmjet = mpl.cm.jet
cmjet = mpl.cm.RdYlBu_r
cmjet.set_bad('white')
cmjet.set_under('white')
cmjet.set_over('black')
topbounds = [0.1,0.50,0.8,0.45]
bottombounds = [0.1,0.05,0.8,0.464]
# centerx, centery, radius, width, height
pl.figure(1,figsize=(12,12))
pl.clf()
dens1 = FITSFigure(parcubefile,convention='calabretta',slices=[0],figure=pl.figure(1),subplot=topbounds)
dens1.show_colorscale(vmin=2,vmax=6,cmap=cmhot)
dens1.recenter(**zoomargs)
dens1.colorbar._colorbar_axes.set_ylabel('log$_{10}$(n(H$_2$) cm$^{-3}$)')
dens1.hide_xaxis_label()
dens1.hide_xtick_labels()
velo1 = FITSFigure(parcubefile,convention='calabretta',slices=[4],figure=pl.figure(1),subplot=bottombounds)
velo1.show_colorscale(vmin=52,vmax=66,cmap=cmjet)
velo1.recenter(**zoomargs)
velo1.colorbar._colorbar_axes.set_ylabel('Velocity ($V_{LSR}$ km s$^{-1}$)')
savefig('W51_H2CO_2parfittry11_v1_densityvelocity%s.png' % extrastr,bbox_inches='tight')
dens1.remove_colorbar()
dens1.hide_colorscale()
col1 = FITSFigure(parcubefile,convention='calabretta',slices=[1],figure=pl.figure(1),subplot=topbounds)
col1.show_colorscale(vmin=11,vmax=13.5,cmap=cmhot)
tbl.add_column(Column(data=tbl['{0} Flux Over Threshold'.format(stat)] / total_co_slab_reg.value,
dtype='float', name='{0} F_total(slab)'.format(stat)))
pkfrac = cube13ss.with_mask(cubemask).spectral_slab(vrange1[0], vrange2[1])[slices].sum().value / total_co_slab_reg.value
tbl.meta['Peak Fraction'] = pkfrac
tables[region] = tbl
if __name__ == "__main__":
from aplpy_figure_maker import FITSFigure
import aplpy
import pylab as pl
fig = pl.figure(1, figsize=(12,12))
fig.clf()
F1 = FITSFigure(cube13ss_slab3_masked_mom0.hdu, subplot=[0.05,0.5,0.4,0.4], figure=fig)
F1.show_grayscale()
F1._ax1.set_title("$^{13}$CO masked with H$_2$CO")
F2 = FITSFigure(cube13_slab3_masked_mom0.hdu, subplot=[0.50,0.5,0.4,0.4], figure=fig)
F2.show_grayscale()
F2._ax1.set_title("$^{13}$CO masked by S/N")
F3 = FITSFigure(high5e4dens_co_slab3_mom0.hdu, subplot=[0.05,0.05,0.4,0.4], figure=fig)
F3.show_grayscale()
F3._ax1.set_title("$^{13}$CO masked by $n>5\\times10^4$")
F4 = FITSFigure(high1e4dens_co_slab3_mom0.hdu, subplot=[0.50,0.05,0.4,0.4], figure=fig)
F4.show_grayscale()
F4._ax1.set_title("$^{13}$CO masked by $n>1\\times10^4$")
F13 = FITSFigure(high1e5dens_co_slab3_mom0.hdu, subplot=[0.05,0.05,0.4,0.4], figure=fig)
F13.show_grayscale()
pl.matplotlib.rc_file('pubfiguresrc')