def calc_geom(sn, field):
"""Calculate the location of bins for a given target S/N and field. """
binsfile = "voronoi_sn{0}_w4500_5500.fits".format(sn, w1, w2)
binimg = pf.getdata(binsfile)
ydim, xdim = binimg.shape
intens = "collapsed_w4500_5500.fits".format(w1, w2)
extent = calc_extent(intens)
extent = offset_extent(extent, field)
x = np.linspace(extent[0], extent[1], xdim)
y = np.linspace(extent[2], extent[3], ydim)
xx, yy = np.meshgrid(x, y)
binimg = np.ma.array(binimg, mask=~np.isfinite(binimg))
bins = np.arange(binimg.min(), binimg.max()+1)
xcen = np.zeros_like(bins)
ycen = np.zeros_like(xcen)
bins = bins.astype(int)
for i, bin in enumerate(bins):
idx = np.where(binimg == bin)
xcen[i] = np.mean(xx[idx])
ycen[i] = np.mean(yy[idx])
radius = np.sqrt(xcen**2 + ycen**2)
pa = np.rad2deg(np.arctan2(xcen, ycen))
# Converting to strings
xcen = ["{0:.5f}".format(x) for x in xcen]
ycen = ["{0:.5f}".format(x) for x in ycen]
radius = ["{0:.5f}".format(x) for x in radius]
pa = ["{0:.5f}".format(x) for x in pa]
specs = np.array(["{0}_bin{1}".format(field, x) for x in bins])
return np.column_stack((specs, xcen, ycen, radius, pa))
def calc_radius(sn, w1, w2, field):
"""Calculate the radius of the binning for a given target S/N. """
binsfile = "voronoi_sn{0}_w{1}_{2}.fits".format(sn, w1, w2)
binimg = pf.getdata(binsfile)
ydim, xdim = binimg.shape
intens = "collapsed_w{0}_{1}.fits".format(w1, w2)
extent = calc_extent(intens)
extent = offset_extent(extent, field)
xtmp = np.linspace(extent[0], extent[1], xdim)
ytmp = np.linspace(extent[2], extent[3], ydim)
xx, yy = np.meshgrid(xtmp, ytmp)
binimg = np.ma.array(binimg, mask=~np.isfinite(binimg))
bins = np.arange(binimg.min(), binimg.max()+1)
radius = np.zeros_like(bins)
for i, bin in enumerate(bins):
idx = np.where(binimg == bin)
xcen = np.mean(xx[idx])
ycen = np.mean(yy[idx])
radius[i] = np.sqrt(xcen**2 + ycen**2)
return radius
k06SII_agn = lambda x: 0.72 / (x - 0.32) + 1.3
k06SII_liner = lambda x: 1.89 * x + 0.76
fig = plt.figure(figsize=(8,8))
plt.subplots_adjust(left=0.08, right=0.97, bottom=0.065, top=0.975,
wspace=0.25)
for sn, field in zip(targetSN, fields):
os.chdir(os.path.join(data_dir, "combined_{0}".format(field)))
######################################################################
# Load images and prepare images
binsfile = "voronoi_sn{0}_w{1}_{2}.fits".format(sn, w1, 5500)
binimg = pf.getdata(binsfile)
ydim, xdim = binimg.shape
intens = "collapsed_w{0}_{1}.fits".format(w1, 5500)
flux = pf.getdata(intens)
extent = calc_extent(intens)
extent = offset_extent(extent, field)
xtmp = np.linspace(extent[0], extent[1], xdim)
ytmp = np.linspace(extent[2], extent[3], ydim)
xx, yy = np.meshgrid(xtmp, ytmp)
binimg = np.ma.array(binimg, mask=~np.isfinite(binimg))
bins = np.arange(binimg.min(), binimg.max() + 1)
r = np.zeros_like(bins)
for i, bin in enumerate(bins):
idx = np.where(binimg == bin)
xcen = np.mean(xx[idx])
ycen = np.mean(yy[idx])
r[i] = np.sqrt(xcen ** 2 + ycen ** 2)
######################################################################
# Data: HB, [OIII], HA, [NII], [SII] lines
filename = "ppxf_emission_sn{2}_w{0}_{1}.txt".format(w1, w2, sn)
data = np.loadtxt(filename, usecols=(6,10,12,16,20,22))