/
plot_lick_radius.py
executable file
·441 lines (425 loc) · 19 KB
/
plot_lick_radius.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Aug 12 10:24:35 2013
@author: cbarbosa
Program to produce plots of Lick indices in 1D, comparing with results from
Coccato et al. 2011
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
from scipy.optimize import curve_fit
from scipy.interpolate import LinearNDInterpolator as interpolator
from matplotlib.colors import Normalize
from scipy import ndimage
import brewer2mpl
from config import *
from mcmc_model import get_model_lims
import newcolorbars as nc
class Ssp:
""" Wrapper for the interpolated model."""
def __init__(self, model_table, indices=np.arange(25)):
self.interpolate(model_table)
self.indices = indices
def interpolate(self, model_table):
modeldata = np.loadtxt(model_table, dtype=np.double)
self.model = interpolator(modeldata[:,:3], modeldata[:,3:])
def fn(self, age, metallicity, alpha):
return self.model(age, metallicity, alpha)[self.indices]
def __call__(self, pars):
return self.fn(*pars)
def get_model_range(table):
""" Get the range for the indices according to models. """
modeldata = np.loadtxt(table)
indices = modeldata[:,3:].T
ranges = np.zeros((len(indices), 2))
for i, index in enumerate(indices):
ranges[i] = [index.min(), index.max()]
return ranges
def line(x, zp, grad ):
return zp + grad * x
def mask_slits():
data = np.loadtxt("results.tab", dtype=str)
mask = ["inn1_s22", "inn1_s25", "inn1_s27", "out1_s19", "out1_s20",
"out1_s21", "out1_s22","out1_s23", "out1_s24", "out1_s25",
"out1_s26", "inn2_s39", "cen1_s14", "cen2_s15", "out2_s22",
"out2_s29", ]
# mask = ["inn1_s22", "inn1_s25", "inn1_s27", "out1_s19", "out1_s20",
# "out1_s21", "out1_s22","out1_s23", "out1_s24", "out1_s25",
# "out1_s26", "inn2_s39", "cen1_s14", "cen2_s15", "inn2_s34",
# "out1_s18", "cen1_s35", "cen2_s23", ]
mask = np.array(["fin1_n3311{0}.fits".format(x) for x in mask])
mask = data[~np.in1d(data[:,0], mask)]
np.savetxt("results_masked.tab", mask, fmt="%s")
return "results_masked.tab"
def movingrms(x, y, window_size=10):
a = np.column_stack((x,y))
a = a[np.argsort(a[:,0])]
window = np.ones(window_size)/float(window_size)
rms = np.sqrt(np.convolve(a[:,1], window, 'same'))
rms = ndimage.filters.gaussian_filter(rms, 2.5)
b = np.column_stack((a[:,0], rms))
b = b[~np.isnan(rms)]
return b
if __name__ == "__main__":
model_table = os.path.join(tables_dir, "models_thomas_2010.dat")
ssp = Ssp(model_table)
restrict_pa = 0
log = True
pc = 1
r_tran = np.log10( 8.4 / re)
plt.ioff()
model_table = os.path.join(tables_dir, "models_thomas_2010.dat")
ranges = get_model_range(model_table)
ii = [12,13,16,17,18,19]
ranges = np.array([ranges[i] for i in ii ])
os.chdir(os.path.join(home, "single2"))
indices = [r"H$\beta$ [$\AA$]", r"Fe5015 [$\AA$]", r"Mg $b$ [$\AA$]",
r"Fe5270 [$\AA$]",r"Fe5335 [$\AA$]",r"Fe5406 [$\AA$]",
r"Fe5709 [$\AA$]"]
lodo_table = os.path.join(tables_dir, "coccato2011_indices.tsv")
lodo = np.loadtxt(lodo_table, usecols=(1,3,5,7,9,11,13,15))
lodoerr = np.loadtxt(lodo_table, usecols=(1,4,6,8,10,12,14,16))
with open(lodo_table) as f:
header = f.readline() [:-1]
# Converting radius to effective units
lodo[:,0] /= (4.125 * re)
if log:
lodo[:,0] = np.log10(lodo[:,0])
#############################
# Applying offsets from paper
lodo[:,1] += 0.11
lodo[:,3] += 0.13
#############################
# Calculating composite indices for Lodo's data
fe5270 = lodo[:,3]
fe5270_e = lodoerr[:,3]
fe5335 = lodo[:,4]
fe5335_e = lodoerr[:,4]
mgb = lodo[:,2]
mgb_e = lodoerr[:,2]
meanfe = 0.5 * (fe5270 + fe5335)
meanfeerr = 0.5 * np.sqrt(fe5270_e**2 + fe5335_e**2)
term = (0.72 * fe5270 + 0.28 * fe5335)
mgfeprime = np.sqrt(mgb * term)
mgfeprimeerr = 0.5 * np.sqrt(term / mgb * (mgb_e**2) +
mgb / term * ((0.72 * fe5270_e)**2 + (0.28 * fe5335_e)**2))
lodo2 = np.column_stack((lodo[:,0], lodo[:,1], lodo[:,3], meanfe,
mgfeprime))
lodo2err = np.column_stack((lodo[:,0], lodoerr[:,1], lodoerr[:,3],
meanfeerr, mgfeprimeerr))
objs = np.loadtxt(lodo_table, dtype = str, usecols=(0,))
lododata = np.loadtxt(lodo_table, usecols=np.arange(1,17))
outtable = np.column_stack((lododata, meanfe, meanfeerr,
mgfeprime, mgfeprimeerr))
outtable = np.around(outtable, decimals=4)
outtable = np.column_stack((objs, outtable))
header += "\t<Fe>\terr\t[MgFe]'\terr\n"
with open(os.path.join(tables_dir, "coccato2011.dat"), "w") as f:
f.write(header)
np.savetxt(f, outtable, fmt="%s")
################################
dwarf = lodo[-1]
lodo = lodo[:-1]
dwarferr = lodoerr[-1]
lodoerr = lodoerr[:-1]
dwarf2 = lodo2[-1]
lodo2 = lodo2[:-1]
dwarf2err = lodo2err[-1]
lodo2err = lodo2err[:-1]
##########################################################################
# Central values according to Loubser+ 2009
loubser = np.array([1.581, 5.03, 4.608, 2.773, 2.473, 1.532, 0.876])
loubser_err = np.array([0.111, 0.228, 0.091, 0.088, 0.099, 0.072, 0.05])
##########################################################################
# Data from Loubser + 2012
loubser12 = np.loadtxt("/home/kadu/Dropbox/hydra1/loubser2012/"
"lick_loubser2012.txt",
usecols=(0,13,14,17,18,19,20,21))
loubser12[:,0] += np.log10(26.6/re) #Scaling to our effective radius
loubser12_errs = np.loadtxt("/home/kadu/Dropbox/hydra1/loubser2012/"
"lick_loubser2012_errs.txt",
usecols=(0,13,14,17,18,19,20,21))
##########################################################################
# Mask table
results_masked = mask_slits()
##########################################################################
# Read data
r, pa, sn, mu = np.loadtxt(results_masked, usecols=(3,4,14,82)).T
r /= re # Normalization to effective radius
if log:
r = np.log10(r)
lick = np.loadtxt(results_masked, usecols=(39,41,47,49,51,53,55))
lickerr = np.loadtxt(results_masked, usecols=(40,42,48,50,52,54,56))
if restrict_pa:
good_pa = np.logical_or(np.logical_and(pa > 48, pa < 78), r < r_tran)
r = r[good_pa]
lick = lick[good_pa]
lickerr = lickerr[good_pa]
sn = sn[good_pa]
r = r[sn > sn_cut]
lick = np.transpose(lick[sn > sn_cut])
lickerr = np.transpose(lickerr[sn > sn_cut])
#########################################################################
# Bin data for gradients
if log:
rbinnum, redges = np.histogram(r, bins=8, range=(r_tran,r.max()))
else:
rbinnum, redges = np.histogram(r, bins=8, range=(10**(r_tran),10**.8))
data_r = []
rbins = []
errs_r = []
lick_masked = np.ma.array(lick, mask=np.isnan(lick))
lickerrs_masked = np.ma.array(lickerr, mask=np.isnan(lick))
for i, bin in enumerate(rbinnum):
idx = np.logical_and(r >= redges[i], r < redges[i+1])
if not len(np.where(idx)[0]):
continue
median = True
if median:
m = np.ma.median(lick_masked[:,idx].T, axis=0) # median
data_r.append(m)
rbins.append(np.ma.median(r[idx], axis=0))
else:
data_r.append(np.ma.average(lick_masked[:,idx].T, axis=0,
weights=np.power(10, -0.4*mu[idx])))
rbins.append(np.ma.average(r[idx], axis=0,
weights=np.power(10, -0.4*mu[idx])))
sigma_mad = 1.4826 * np.ma.median(np.abs(lick_masked[:,idx].T - m),
axis=0)
sigma = np.ma.std(lick_masked[:,idx].T, axis=0)
errs_r.append(sigma_mad)
data_r = np.array(data_r)
rbins = np.array(rbins)
errs_r = np.array(errs_r)
#########################################################################
# Taking only inner region for gradients in NGC 3311
if log:
idx3311 = np.where(r <= r_tran)[0]
idxhalo = np.where(r > r_tran)[0]
else:
idx3311 = np.where(r <= 10**(r_tran))[0]
idxhalo = np.where(r > 10**(r_tran))[0]
r3311 = r[idx3311]
rhalo = r[idxhalo]
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])
ydata = ll[notnans]
ax.errorbar(r[notnans], ydata, yerr=lickerr[j][notnans],
fmt=None, color=gray, ecolor=gray, capsize=0, mec=gray,
ms=5.5, alpha=1, markerfacecolor="none",
mew=2, elinewidth=1 )
ax.scatter(r[notnans], ydata, c=sn[notnans], s=60, cmap=cmap, zorder=2,
lw=0.5, norm=norm, edgecolor="k")
ax.plot(1000, 1000, "o", mew=0.8, mec="k", c=color(0),
label=r"S/N $< 15$")
ax.plot(1000, 1000, "o", mew=0.8, mec="k", c=color(0.5),
label=r"$15\leq$ S/N $\leq 30$")
ax.plot(1000, 1000, "o", mew=0.8, c=color(1.), mec="k",
label=r"S/N $> 30$")
ax.errorbar(loubser12[:,0], loubser12[:,j+1],
yerr=loubser12_errs[:,j+1], color="r", ecolor="r",
fmt="s", mec="k", capsize=0, lw=0.2,
label= "Loubser et al. 2012", alpha=1, ms=7.5, mew=0.5)
ax.errorbar(lodo[:,0], lodo[:,j+1],
yerr = lodoerr[:,j+1],
fmt="^", c="orange", capsize=0, mec="k", ecolor="0.5",
label=labels[1], ms=8., alpha=1, lw=0.5, mew=0.5)
ax.errorbar(dwarf[0],
dwarf[j+1], yerr=dwarferr[j+1], fmt="^", c="orange",
capsize=0, mec="k", ecolor="0.5", ms=8., lw=0.5, mew=0.5)
plt.minorticks_on()
if j+1 != len(lick):
ax.xaxis.set_ticklabels([])
else:
plt.xlabel(r"$\log$ R / R$_{\mbox{e}}$")
plt.ylabel(indices[j])
ax.yaxis.set_major_locator(plt.MaxNLocator(5))
if j == 0:
leg = ax.legend(prop={'size':11}, loc=2, ncol=2, fontsize=14,
scatterpoints = 1, frameon=False)
add = 0 if j != 0 else 2
sigma_mad = 1.48 * np.median(np.abs(ydata - np.median(ydata)))
ym = np.ceil(np.median(ydata)-4 * sigma_mad)
yp = np.floor(np.median(ydata)+4*sigma_mad+add)
ylim = plt.ylim(ym, yp)
##################################################################
# Measuring gradients
##################################################################
# NGC 3311
l = lick3311[j]
lerr = errs1_3311[j]
mask = ~np.isnan(l)
popt, pcov = curve_fit(line, r3311[mask], l[mask], sigma=lerr[mask])
pcov = np.sqrt(np.diagonal(pcov))
x = np.linspace(r.min(), r_tran, 100)
if not log:
x = 10**x
y = line(x, popt[0], popt[1])
lll, = ax.plot(x, y, "--k", lw=2, zorder=10000)
lll.set_dashes([10, 3])
# Including shades for +- 1%'
##################################################################
# Halo
values = lickhalo[j]
for k,v in enumerate(values):
if v <= lims[j][0] or v >= lims[j][1]:
values[k] = np.nan
mask = ~np.isnan(values)
l = lickhalo[j][mask]
lerr = errs_halo[j][mask]
popth, pcovh = curve_fit(line, rhalo[mask], l, sigma=lerr)
pcovh = np.sqrt(np.diagonal(pcovh))
x = np.linspace(r_tran, 0.7, 100)
if not log:
x = 10**x
y = line(x, popth[0], popth[1])
lll, = ax.plot(x, y, "-k", lw=1.5, zorder=10000)
lll.set_dashes([10, 3])
#################################################################
# Ploting rms 1%
for p, c in [[1,"0.3"]]:
tab = os.path.join(tables_dir,
"rms_{1}pc_lick_{0}.txt".format(j, p))
print tab
rms = np.loadtxt(os.path.join(tables_dir,
"rms_{1}pc_lick_{0}.txt".format(j, p)),
usecols=(0,1))
xrms, yrms = rms[rms[:,0] < r_tran].T
# ax.plot(xrms, yrms + line(xrms, popt[0], popt[1]), "-", c="0.5")
# ax.plot(xrms, -yrms + line(xrms, popt[0], popt[1]), "-", c="0.5")
ax.fill_between(xrms, yrms + line(xrms, popt[0], popt[1]),
line(xrms, popt[0], popt[1]) - yrms,
edgecolor="none", color=gray,
linewidth=0, alpha=1)
##################################################################
# Outer halo in bins
# popt2, pcov2 = curve_fit(line, rbins, data_r[:,j], sigma=errs_r[:,j])
# pcov2 = np.sqrt(np.diagonal(pcov2))
# x = np.linspace(r_tran, r.max(), 100)
# if not log:
# x = 10**x
# y = line(x, popt2[0], popt2[1])
# ax.plot(x, y, "--k", lw=2)
# ax.axvline(x=r_tran, c="k", ls="-.")
##################################################################
# Ploting rms 1%
# for p, c in [[1,"0.1"], [6, "0.8"]]:
for p, c in [[1,"0.1"]]:
rms = np.loadtxt(os.path.join(tables_dir,
"rms_{1}pc_lick_{0}.txt".format(j, p)),
usecols=(0,1))
xrms, yrms = rms[rms[:,0]>=r_tran].T
ax.fill_between(xrms, yrms + line(xrms, popth[0], popth[1]),
line(xrms, popth[0], popth[1]) - yrms,
edgecolor="none", color=gray,
linewidth=0, alpha=1)
##################################################################
# Draw arrows to indicate central limits
ax.annotate("", xy=(-1.12, loubser[j]), xycoords='data',
xytext=(-1.3, loubser[j]), textcoords='data',
arrowprops=dict(arrowstyle="<-", connectionstyle="arc3", ec="r",
lw=2))
##################################################################
ax.set_xlim(-1.35, 1.)
# ##################################################################
tex.append(r"{0} & {1[0]:.1f}$\pm${2[0]:.1f} & {1[1]:.1f}$\pm${2[1]:.1f}" \
r" & {3[0]:.1f}$\pm${4[0]:.1f} & {3[1]:.1f}$\pm${4[1]:.1f}""\\\\".format(
indices[j][:-7], popt, pcov, popth, pcovh))
print indices[j][:-7],
for m in [1,2,3]:
print np.abs(popt[1] - popth[1]) < m * (pcov[1]+pcovh[1]),
print
print "Saving new figure..."
plt.savefig("figs/lick_radius.png".format(pc), dpi=300,
bbox_inches="tight", transparent=False)
for t in tex:
print t
# plt.show(block=1)
# Making plots of Hbeta, Mgb, <Fe> and [MgFe]'
# r, pa, sn = np.loadtxt(results_masked, usecols=(3,4,14)).T
# # r /= re
# lick = np.loadtxt(results_masked, usecols=(39, 67, 80))
# lickerr = np.loadtxt(results_masked, usecols=(40, 68, 81))
# if restrict_pa:
# good_pa = np.logical_and(pa > 0, pa < 270)
# r = r[good_pa]
# lick = lick[good_pa]
# lickerr = lickerr[good_pa]
# sn = sn[good_pa]
# r = r[sn > sn_cut]
# lick = np.transpose(lick[sn > sn_cut])
# lickerr = np.transpose(lickerr[sn > sn_cut])
# gs2 = gridspec.GridSpec(len(lick),3)
# gs2.update(left=0.15, right=0.95, bottom = 0.1, top=0.94, hspace = 0.10,
# wspace=0.04)
# plt.figure(2, figsize = (6, 7))
# indices = [r"H$\beta$ [$\AA$]", r"[MgFe]'",
# r"$\mbox{Mg }b/\langle\mbox{Fe}\rangle$"]
#
# for j, (ll,lerr) in enumerate(zip(lick, lickerr)):
# ax = plt.subplot(gs2[j, 0:2], xscale="log")
# notnans = ~np.isnan(ll)
# ax.errorbar(r[notnans], ll[notnans], yerr=lerr[notnans],
# fmt="d", color="r",
# ecolor=gray, capsize=0, mec="k", markerfacecolor="none")
# # plt.errorbar(lodo2[:,0], lodo2[:,j+1],
# # yerr=lodo2err[:,j+1], fmt="+", c="b", capsize=0,
# # mec="b", ecolor="0.5", label=None, ms=10)
# # plt.errorbar(dwarf2[0],
# # dwarf2[j+1], yerr=dwarf2err[j+1], fmt="o",
# # c="w", capsize=0, mec="b", ecolor="0.5")
# plt.minorticks_on()
# if j != len(lick) -1 :
# ax.xaxis.set_ticklabels([])
# else:
# plt.xlabel(r"R (kpc)")
# ax.set_xticklabels(["0.1", "1", "10"])
# plt.ylabel(indices[j], fontsize=10)
# ax.yaxis.set_major_locator(plt.MaxNLocator(5))
# plt.ylim(ylims[j])
# # Histograms
# ax = plt.subplot(gs2[j, 2])
# plt.minorticks_on()
# ax.hist(ll[notnans], orientation="horizontal", color="r",
# ec="k")
# ax.yaxis.set_major_locator(plt.MaxNLocator(5))
# ax.xaxis.set_ticklabels([])
# ax.yaxis.set_ticklabels([])
# plt.ylim(ylims[j])
# plt.savefig("figs/lick_radius_combined.pdf",
# bbox_inches="tight", transparent=False)