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')
