def make_mag(targetSN, w1, w2):
tables = []
for i in range(len(targetSN)):
tables.append("magab_w{0}_{1}.dat".format(w1, w2))
columns = (2,)
lims = [[19, 23]]
cmaps = [nc.cmap_discretize(cm.get_cmap("hot"), 8)]
labels = ["$m_{V}$"]
cb_fmts = ["%.1f"]
yloss = [0.5]
names = ["magv"]
plot(targetSN, 4500, 5500, tables, names, columns, lims, cmaps, labels,
cb_fmts, yloss)
return
def make_ssps_individual(targetSN, field, model, w1, w2):
""" Make maps for the kinematics. """
filename = "logs_sn{2}/mcmc_{0}_{1}.txt".format(field, model, targetSN)
binimg = pf.getdata("voronoi_sn{0}_w{1}_{2}.fits".format(targetSN, w1, w2))
intens = "collapsed_w{0}_{1}.fits".format(w1, w2)
extent = calc_extent(intens)
bins = np.loadtxt(filename, usecols=(0,), dtype=str).tolist()
bins = np.array([x.split("bin")[1] for x in bins]).astype(int)
data = np.loadtxt(filename, usecols=(1,4,7,4)).T
data[3] -= 0.94 * data[1]
cmaps = [brewer2mpl.get_map('Reds', 'sequential', 6).mpl_colormap,
brewer2mpl.get_map('Blues', 'sequential', 6).mpl_colormap,
brewer2mpl.get_map('Greens', 'sequential', 6).mpl_colormap,
brewer2mpl.get_map('Oranges', 'sequential', 6).mpl_colormap]
# cmaps = [nc.cmap_map(lambda x: x*0.54 + 0.43, x) for x in cmaps]
cmaps = [nc.cmap_discretize(x, 6) for x in cmaps]
labels = [r'$\log $ Age (yr)', r'[Z/H]', r"[$\alpha$/Fe]", r"[Fe/H]"]
lims = [[None, None], [None, None], [None, None], [None, None]]
pdf = PdfPages("figs/populations_sn{0}.pdf".format(targetSN))
fig = plt.figure(1, figsize=(6.25,5))
plt.subplots_adjust(bottom=0.12, right=0.965, left=0.09, top=0.96)
plt.minorticks_on()
ax = plt.subplot(111)
ax.minorticks_on()
for i, vector in enumerate(data):
print "Making plot for {0}...".format(labels[i])
kmap = np.zeros_like(binimg)
kmap[:] = np.nan
for bin,v in zip(bins, vector):
idx = np.where(binimg == bin)
kmap[idx] = v
vmin = lims[i][0] if lims[i][0] else np.median(vector) - 1 * vector.std()
vmax = lims[i][1] if lims[i][1] else np.median(vector) + 1 * vector.std()
m = plt.imshow(kmap, cmap=cmaps[i], origin="bottom", vmin=vmin,
vmax=vmax, extent=extent, aspect="equal")
make_contours()
plt.minorticks_on()
plt.xlabel("X [kpc]")
plt.ylabel("Y [kpc]")
plt.xlim(extent[0], extent[1])
plt.ylim(extent[2], extent[3])
cbar = plt.colorbar(m)
cbar.set_label(labels[i])
pdf.savefig()
plt.clf()
pdf.close()
return
lick3311 = lick[:, idx3311]
lickhalo = lick[:, idxhalo]
errs1_3311 = lickerr[:, idx3311]
errs_halo = lickerr[:, idxhalo]
#########################################################################
# First figure, simple indices
app = "_pa" if restrict_pa else ""
mkfig1 = True
gray = "0.75"
##########################################################################
lims, ranges = get_model_lims(os.path.join(tables_dir, "models_thomas_2010_metal_extrapolated.dat"))
idx = np.array([12, 13, 16, 17, 18, 19, 20])
lims = lims[idx]
# Setting the colormap properties for the scatter plots
cmap = brewer2mpl.get_map("Blues", "sequential", 9).mpl_colormap
cmap = nc.cmap_discretize(cmap, 3)
color = cm.get_cmap(cmap)
norm = Normalize(vmin=0, vmax=45)
if mkfig1:
plt.figure(1, figsize=(6, 14))
gs = gridspec.GridSpec(7, 1)
gs.update(left=0.15, right=0.95, bottom=0.1, top=0.94, wspace=0.1, hspace=0.09)
tex = []
for j, ll in enumerate(lick):
# print indices[j], ranges[j], ssp.fn(9.,0.12,.4)[ii[j]]
if j == 0:
labels = ["This work", "Coccato et al. 2011", "This work (binned)"]
else:
labels = [None, None, None]
notnans = ~np.isnan(ll)
ax = plt.subplot(gs[j])
lickhalo = lick[:, idxhalo]
errs1_3311 = lickerr[:, idx3311]
errs_halo = lickerr[:, idxhalo]
#########################################################################
# First figure, simple indices
app = "_pa" if restrict_pa else ""
mkfig1 = True
gray = "0.75"
##########################################################################
lims, ranges = get_model_lims(
os.path.join(tables_dir, "models_thomas_2010_metal_extrapolated.dat"))
idx = np.array([12, 13, 16, 17, 18, 19, 20])
lims = lims[idx]
# Setting the colormap properties for the scatter plots
cmap = brewer2mpl.get_map('Blues', 'sequential', 9).mpl_colormap
cmap = nc.cmap_discretize(cmap, 3)
color = cm.get_cmap(cmap)
norm = Normalize(vmin=0, vmax=45)
if mkfig1:
plt.figure(1, figsize=(6, 14))
gs = gridspec.GridSpec(7, 1)
gs.update(left=0.15,
right=0.95,
bottom=0.1,
top=0.94,
wspace=0.1,
hspace=0.09)
tex = []
for j, ll in enumerate(lick):
# print indices[j], ranges[j], ssp.fn(9.,0.12,.4)[ii[j]]
if j == 0:
