def make_table(field, targetSN, w1, w2):
""" Joins tables of emission lines. """
bins = halo_bins(field)
data = []
for i, bin in enumerate(bins):
logfile = "logs_sn{0}_w{1}_{2}/emission_bin{3:04d}.txt".format(
targetSN, w1, w2, bin)
with open(logfile) as f:
content = f.readlines()
header = [x.strip() for x in content if x.startswith("#")]
data.append([x for x in content if not x.startswith("#")][0])
with open("emission_sn{0}_w{1}_{2}.txt".format(targetSN,
w1, w2, bin), "w") as f:
f.write("\n".join(header + data))
if j == 0:
plt.ylabel("Flux ($10^{-20}$ erg / s / $\AA$)")
ax.set_xlabel("$\lambda$ ($\AA$)")
plt.legend(prop={"size":9})
ylim = ax.get_ylim()
ax.set_ylim(ylim[0], 1.5*ylim[1])
plt.locator_params(axis='x', nbins=5)
plt.subplots_adjust(left=0.04, right=0.99, top=0.95)
plt.savefig("logs_sn{0}_w{1}_{2}/emission_bin{3:04d}.png".format(
targetSN, w1, w2, bin))
###############################################################################
if __name__ == "__main__":
targetSN = 70
w1 = 4500
w2 = 7000
fwhm = 2.95
sigma_res = fwhm / (2. * np.sqrt(2. * np.log(2.)))
lines = (("Hbeta_4861", 4861.333), ("OIII_4959", 4958.91),
("OIII_5007", 5006.84), ("Halpha_6565", 6564.61),
("NI_5200", 5200.257), ("NII_6585", 6585.27),
("NII_6550", 6549.86), ("SII_6718", 6718.29),
("SII_6733", 6732.67))
for field in fields:
os.chdir(os.path.join(data_dir, "combined_{0}".format(field)))
# prepare_residuals(field, w1, w2, targetSN, clobber=True)
bins = halo_bins(field)
# fit(field, targetSN, w1, w2, bins=bins, redo=False)
# make_table(field, targetSN, w1, w2)
prepare_table_for_map(field, targetSN, w1, w2)
break
def plot_rad_kinematics(targetSN, w1, w2, only_halo=False, fors2=True,
label="MUSE"):
""" Plot LOSVD moments as a function of the radius. """
###########################################################################
# Loading data into a single array
cols = np.array((7,9, 11))
for i, (sn, field) in enumerate(zip(targetSN, fields)):
wdir = os.path.join(data_dir, "combined_{0}".format(field))
os.chdir(wdir)
table = "results_sn{0}_w{1}_{2}.dat".format(sn, w1, w2)
specs = np.loadtxt(table, usecols=(0,), dtype=str)
rr = np.loadtxt(table, usecols=(3,))
dat = np.loadtxt(table, usecols=cols)
err = np.loadtxt(table, usecols=cols+1)
c = np.loadtxt(table, usecols=(4,))
if only_halo:
bins = np.array([int(x.split("bin")[1]) for x in specs])
hbins = halo_bins(field)
idx = np.in1d(bins, hbins)
rr = rr[idx]
dat = dat[idx]
err = err[idx]
c = c[idx]
if i == 0:
r = rr
data = dat
error = err
colors = c
else:
r = np.hstack((r, rr))
data = np.vstack((data, dat))
error = np.vstack((error, err))
colors = np.hstack((colors, c))
###########################################################################
# Reading data from previous paper
table = os.path.join(tables_dir, "Barbosa2016_lick_instr_res.dat")
rb16 = np.loadtxt(table, usecols=(3,))
datab16 = np.loadtxt(table, usecols=(7,9,11)).T
errb16 = np.loadtxt(table, usecols=(8,10,12)).T
###########################################################################
labels = ["$\sigma$ (km/s)", "$h_3$", "$h_4$"]
data = data.T
error = error.T
os.chdir(plots_dir)
lims = [[100, 500], [-.2, 0.2], [-.4, .4]]
fig = plt.figure(1, figsize=(5,5))
for i, (l, lerr, l2, l2err) in enumerate(zip(data, error, datab16, errb16)):
ax = plt.subplot(len(cols), 1, i+1)
ax.set_xscale("log")
ax.errorbar(r, l, yerr=lerr, fmt="o", ecolor="0.8", zorder=0,
elinewidth=.5, capsize=0.5, mec="0.2", label=label,
markersize=5.5, c="lightskyblue", markeredgecolor="k",
mew=0.4)
if fors2:
ax.errorbar(rb16, l2, yerr=l2err, fmt="s", ecolor="0.8", zorder=0,
elinewidth=0.5, capsize=0.5, label="FORS2",
markersize=5, linewidth=0.6,
markeredgecolor="k", color="red", mew=0.4)
q1, q2 = np.percentile(l, [5, 95])
ymin = lims[i][0] if lims[i][0] else q1 - 0.6 * np.abs(q2 - q1)
ymax = lims[i][1] if lims[i][1] else q2 + 0.6 * np.abs(q2 - q1)
ax.set_ylim(ymin, ymax)
ax.set_xlim(0.1, 50)
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
if i == len(cols)-1:
ax.set_xlabel("R (kpc)")
else:
ax.xaxis.set_major_formatter(plt.NullFormatter())
if i == 0:
ax.legend(scatterpoints=1, prop={'size':10},
ncol=1, loc=2, frameon=False)
ax.minorticks_on()
ax.set_ylabel(labels[i])
if i in [1,2]:
ax.axhline(y=0, ls="--", c="k")
plt.subplots_adjust(left=0.15, right=0.96, wspace=0.38, top=0.98,
hspace=0.1, bottom=0.1)
name = "_halo" if only_halo else "all"
forsstring = "_fors2" if fors2 else ""
plt.savefig(os.path.join(plots_dir, "rad_losvd{0}{1}_w{2}_{3}.png".format(
name, forsstring, w1, w2)), dpi=150)
plt.clf()
return
def merge_tables(targetSN, w1, w2):
""" Make single table containing kinematics and stellar populations. """
for sn, field in zip(targetSN, fields):
print "Merging {0}".format(field)
wdir = os.path.join(data_dir, "combined_{0}".format(field))
os.chdir(wdir)
######################################################################
# Calculating geometric parameters of bins
geom = calc_geom(sn, field)
######################################################################
# Selecting tables
kintable = "ppxf_results_sn{0}_w{1}_{2}.txt".format(sn, w1, w2)
licktab = "lick_corr_sn{0}_w{1}_{2}.txt".format(sn, 4500, 7000)
lickerrtab = "lick_mcerr_sn{0}_w{1}_{2}.txt".format(sn, 4500, 7000)
magtab = "magab_w5000_6000.dat"
# poptable = os.path.join(data_dir, "populations_chain1.txt")
tables = [kintable, licktab, lickerrtab, magtab]
# #####################################################################
# Loading data in tables
data = [np.loadtxt(x, dtype=str) for x in tables]
# Concatenating data from geometric calculations
data = [geom] + data
######################################################################
# Aligning tables
specs = [np.array([int(y.split("bin")[1]) for y in x[:,0]]) for x in
data]
ref = intersect_n_arrays(specs)
idx = [np.searchsorted(x, ref) for x in specs]
data = [d[i] for (i,d) in zip(idx,data)]
#######################################################################
# Interwine Lick indices and errors
lick = np.insert(data[3], np.arange(len(data[2][0])), data[2], axis=1)
data[2] = lick[:,1:]
del data[3]
#######################################################################
combined = []
for i in range(len(data)):
if i == 0:
combined = data[i]
else:
combined = np.column_stack((combined, data[i][:,1:]))
####################################################################
# Including boolean indication if bin is part of halo or not
inhalo = np.in1d(ref, halo_bins(field)).astype(int)
combined = np.column_stack((combined, inhalo))
# Making headers
header = ["Spectrum", "X(kpc)", "Y(kpc)", "R(kpc)", "PA(degree)", 'V',
'V_err', 'Sigma', 'Sigma_err', 'h3',
'h3_err', 'h4', 'h4_err', 'chi/DOF', 'S/N/Angstrom',
'adegree', 'mdegree']
indices = np.loadtxt(os.path.join(tables_dir, "bands.txt"),
usecols=(0,), dtype=str)
err = ["{0}_err".format(x) for x in indices]
indices = np.insert(err, np.arange(25), indices)
header += indices.tolist()
header += ["mag_ab", "mag_V"]
header += ["is_halo"]
# header += ["Age", "LERR", "UERR", "[Z/H]", "LERR", "UERR", "[alpha]/Fe",
# "LERR", "UERR"]
# header += ["RAJ2000", "DECJ2000"]
header = ["# ({0}) {1}".format(i,h) for i,h in enumerate(header)]
# #####################################################################
filename = os.path.join(wdir, "results_sn{0}_w{1}_{2}.dat".format(
sn, w1, w2))
with open(filename, "w") as f:
f.write("\n".join(header) + "\n")
np.savetxt(f, combined, fmt="%s")
return
def plot_rad_lick(targetSN, w1, w2, only_halo=True, fors2=True):
""" Plot Lick indices as a function of the radius. """
###########################################################################
# Loading data into a single array
for i, (sn, field) in enumerate(zip(targetSN, fields)):
wdir = os.path.join(data_dir, "combined_{0}".format(field))
os.chdir(wdir)
table = "results_sn{0}_w{1}_{2}.dat".format(sn, w1, w2)
specs = np.loadtxt(table, usecols=(0,), dtype=str)
rr = np.loadtxt(table, usecols=(3,))
l = np.loadtxt(table, usecols=np.arange(41,64,2))
le = np.loadtxt(table, usecols=np.arange(42,65,2))
c = np.ones_like(rr) * i
if only_halo:
bins = np.array([int(x.split("bin")[1]) for x in specs])
hbins = halo_bins(field)
idx = np.in1d(bins, hbins)
rr = rr[idx]
l = l[idx]
le = le[idx]
c = np.ones_like(rr) * i
if i == 0:
r = rr
lick = l
lickerr = le
colors = c
else:
r = np.hstack((r, rr))
lick = np.vstack((lick, l))
lickerr = np.vstack((lickerr, le))
colors = np.hstack((colors, c))
###########################################################################
# Reading data from previous paper
table = os.path.join(tables_dir, "Barbosa2016_lick_instr_res.dat")
rb16 = np.loadtxt(table, usecols=(3,))
lb16 = np.loadtxt(table, usecols=np.arange(39,62,2)).T
lb16err = np.loadtxt(table, usecols=np.arange(40,63,2)).T
###########################################################################
indices = np.loadtxt(os.path.join(tables_dir, "bands.txt"), usecols=(0,),
dtype="S16")
for i, index in enumerate(indices):
if "_" in index:
indices[i] = index.replace("_", "$_").replace("_beta", r"\beta") \
+ "$"
mag = "(mag)"
ang = "($\AA$)"
units = np.array([ang, ang, mag, mag, ang, ang, ang, ang, ang, ang, ang,
ang, ang, ang, mag, mag, ang, ang, ang, ang, ang, ang, ang, mag,
mag])
indices = ["{0} {1}".format(x,y) for x,y in zip(indices, units)]
indices = indices[12:]
lick = lick.T
lickerr = lickerr.T
os.chdir(plots_dir)
fig = plt.figure(1, figsize=(10,6))
cmap = "YlGnBu_r"
for i, (l, lerr, l2, l2err) in enumerate(zip(lick, lickerr, lb16, lb16err)):
ax = plt.subplot(4, 3, i+1)
ax.set_xscale("log")
ax.errorbar(r, l, yerr=lerr, fmt=None, ecolor="0.8", zorder=0,
elinewidth=.5, capsize=0.5)
if not only_halo:
ax.errorbar(rb16, l2, yerr=l2err, fmt=None, ecolor="0.8", zorder=0,
elinewidth=0.5, capsize=0.5)
ax.scatter(r, l, c=colors, s=15, zorder=1, cmap=cmap, vmin=-0.5,
linewidths=0.2)
if fors2:
ax.scatter(rb16, l2, c="r", s=10, zorder=1, marker="s",
linewidths=0.2)
q1, q2 = np.percentile(l, [2, 98])
ax.set_ylim(q1 - 1.2 * np.abs(q2 - q1), q2 + 1.2 * np.abs(q2 - q1) )
ax.set_xlim(0.1, 50)
if i > 8:
ax.set_xlabel("R (kpc)")
else:
ax.xaxis.set_major_formatter(plt.NullFormatter())
if i == 0:
for c, field in zip([0, 0.33, 0.66, 1.], fields):
ax.scatter(1000, 1000, c=cm.get_cmap(cmap)(c), s=15, zorder=1,
cmap=cmap, vmin=-0.5, linewidths=0.2,
label="Field {0}".format(field[-1]))
ax.legend(scatterpoints=1, prop={'size':6},
ncol=2, loc=2)
ax.minorticks_on()
ax.set_ylabel(indices[i])
plt.subplots_adjust(left=0.08, right=0.98, wspace=0.38, top=0.98,
hspace=0.13)
name = "halo" if only_halo else "all"
forsstr = "_fors2" if fors2 else ""
plt.savefig(os.path.join(plots_dir, "rlick_{0}{1}.png".format(name,
forsstr)),
dpi=200)
return
