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paper1.py
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paper1.py
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#!/usr/bin/env python
# encoding: utf-8
"""
paper1.py
Scripts for G. Brammer's first paper with the 3D-HST data. General utilities for redshift fitting, etc. are also in 'unicorn.analysis'.
$URL: https://subversion.assembla.com/svn/threedhst_internal/trunk/reduce.py $
$Author: gbrammer $
$Date: 2011-05-22 02:01:43 -0400 (Sun, 22 May 2011) $
"""
__version__ = " $Rev: 5 $"
import glob
import os
import pyfits
import numpy as np
import matplotlib.pyplot as plt
USE_PLOT_GUI=False
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
import threedhst
import unicorn
sdss_iclass = None
sdss_info = None
sdss_line = None
sdss_totsfr = None
sdss_totspecsfr = None
sdss_logm = None
sdss_fiboh = None
sdss_sersic = None
sdss_vagc = None
idx_mpa_vagc = None
sdss_selection = False
def read_sdss(read_full_lines=True):
"""
Read MPA-JHU SDSS libraries.
If 'read_full_lines' is set, then also read the full line catalog with fluxes etc.
This is a big file so can take more time/memory.
"""
import unicorn.paper1
import cosmocalc
sdss_path = unicorn.GRISM_HOME + 'ANALYSIS/SDSS/'
print 'SDSS ICLASS...'
if unicorn.paper1.sdss_iclass is None:
unicorn.paper1.sdss_iclass = pyfits.open(sdss_path + 'gal_iclass_dr7_v5_2.fits')[0].data
print 'SDSS Info, redshifts etc....'
if unicorn.paper1.sdss_info is None:
unicorn.paper1.sdss_info = pyfits.open(sdss_path + 'gal_info_dr7_v5_2.fits')[1].data
if read_full_lines:
print 'SDSS Full line data...'
if unicorn.paper1.sdss_line is None:
unicorn.paper1.sdss_line = pyfits.open(sdss_path + 'gal_line_dr7_v5_2.fits')[1].data
print 'SDSS Total SFR...'
if unicorn.paper1.sdss_totsfr is None:
unicorn.paper1.sdss_totsfr = pyfits.open(sdss_path + 'gal_totsfr_dr7_v5_2.fits')[1].data
print 'SDSS Total Specific SFR...'
if unicorn.paper1.sdss_totspecsfr is None:
unicorn.paper1.sdss_totspecsfr = pyfits.open(sdss_path + 'gal_totspecsfr_dr7_v5_2.fits')[1].data
print 'SDSS Total stellar mass...'
if unicorn.paper1.sdss_logm is None:
unicorn.paper1.sdss_logm = pyfits.open(sdss_path + 'totlgm_dr7_v5_2.fits')[1].data
print 'SDSS O/H abundances...'
if unicorn.paper1.sdss_fiboh is None:
unicorn.paper1.sdss_fiboh = pyfits.open(sdss_path + 'gal_fiboh_dr7_v5_2.fits')[1].data
print 'SDSS-VAGC Sersic fits...'
if unicorn.paper1.sdss_sersic is None:
unicorn.paper1.sdss_sersic = pyfits.open(sdss_path + 'sersic_catalog.fits')[1].data
#
print 'SDSS-VAGC Coords...'
if unicorn.paper1.sdss_vagc is None:
unicorn.paper1.sdss_vagc = pyfits.open(sdss_path + 'object_catalog.fits')[1].data
print 'Match between catalogs'
if unicorn.paper1.idx_mpa_vagc is None:
unicorn.paper1.idx_mpa_vagc = np.loadtxt(sdss_path + 'mpa_vagc.match', dtype=np.int)
#### Keep only matches from VAGC
unicorn.paper1.sdss_sersic = unicorn.paper1.sdss_sersic[unicorn.paper1.idx_mpa_vagc]
#### Get R50 in kpc
zgrid = np.arange(100)/100.*4+1./100
scale = zgrid*0.
for i in range(100):
cc = cosmocalc.cosmocalc(zgrid[i])
scale[i] = cc['PS_kpc']
unicorn.paper1.sdss_sersic.SERSIC_R50_i_KPC = unicorn.paper1.sdss_sersic.SERSIC_R50[:,3]*np.interp(unicorn.paper1.sdss_info.Z, zgrid, scale)
def match_sersic():
"""
Need to match MPA-JHU catalogs to the VAGC sersic catalog by RA/Dec.
"""
import unicorn.paper1 as p1
N = len(p1.sdss_info)
match_idx = np.zeros(N, dtype=np.int)
idx = np.arange(len(p1.sdss_vagc), dtype=np.int)
in_spectro = p1.sdss_vagc.SDSS_SPECTRO_TAG >= 0
# idx = idx[in_spectro]
# ra_vagc= p1.sdss_vagc.RA[in_spectro]
# de_vagc= p1.sdss_vagc.DEC[in_spectro]
cosdec = np.cos(p1.sdss_info.DEC/360*2*np.pi)
os.chdir('/research/HST/GRISM/3DHST/ANALYSIS/SDSS')
fp = open('mpajhu.radec','w')
for i in range(N):
fp.write(' %13.6f %13.6f\n' %(p1.sdss_info.RA[i], p1.sdss_info.DEC[i]))
fp.close()
fp = open('vagc.radec','w')
for i in range(len(p1.sdss_vagc)):
fp.write(' %13.6f %13.6f\n' %(p1.sdss_vagc.RA[i], p1.sdss_vagc.DEC[i]))
fp.close()
### gcc match.c
### ./a.out
### gzip *.radec
# i=0; j=59367
# js = np.loadtxt('mpa_vagc.match')
# j = js[i]
# dr = 3600.*np.sqrt(((p1.sdss_info.RA[i]-p1.sdss_vagc.RA[j])*np.cos(p1.sdss_info.DEC[i]))**2+(p1.sdss_info.DEC[i]-p1.sdss_vagc.DEC[j])**2)
# print dr
def sdss_selection(zmin=0.001, zmax=0.2, type='GALAXY', massmin=8, massmax=12):
import unicorn.paper1 as p1
#
redshift = (p1.sdss_info.Z >= zmin) & (p1.sdss_info.Z <= zmax) & (p1.sdss_info.Z_WARNING == 0)
#
target_type = p1.sdss_info.TARGETTYPE == type
#
mass_selection = (p1.sdss_logm.AVG >= massmin) & (p1.sdss_logm.AVG <= massmax)
#
selection = redshift & target_type & mass_selection
#
return selection, len(p1.sdss_logm.AVG[selection])
def explore_em_lines():
"""
Look at ratios of OIII4363, 5007, HB, NeIII 3869
some of the selections come from unicorn.paper1
"""
sel, NOBJ = p1.sdss_selection(zmax=0.2)
SN_line_limit = 3
SN_lines = (p1.sdss_line.NII_6584_FLUX/p1.sdss_line.NII_6584_FLUX_ERR > SN_line_limit) & (p1.sdss_line.H_ALPHA_FLUX/p1.sdss_line.H_ALPHA_FLUX_ERR > SN_line_limit) & (p1.sdss_line.OIII_5007_FLUX/p1.sdss_line.OIII_5007_FLUX_ERR > SN_line_limit) & (p1.sdss_line.H_BETA_FLUX/p1.sdss_line.H_BETA_FLUX_ERR > SN_line_limit)
gal_lines = sel & SN_lines # & (p1.sdss_totspecsfr > -10.7)
### BPT diagram
bptx = np.log10( p1.sdss_line.NII_6584_FLUX / p1.sdss_line.H_ALPHA_FLUX )
bpty = np.log10( p1.sdss_line.OIII_5007_FLUX / p1.sdss_line.H_BETA_FLUX )
bpty_doublet = np.log10( (p1.sdss_line.OIII_5007_FLUX + p1.sdss_line.OIII_4959_FLUX ) / p1.sdss_line.H_BETA_FLUX )
### kauffman separation between SF galaxies and AGN
xsf = np.arange(100)/100.*1.5-1.5
ysf = 0.61/(xsf-0.05)+1.3
#### star-forming galaxies
ysf_int = np.interp(bptx, xsf, ysf, right=-10, left=-10)
sfg = (bpty < ysf_int) & (bptx < -0.1) & gal_lines
qui = ((bpty > ysf_int) | (bptx > -0.1)) & sel
agn = (bpty > ysf_int) & gal_lines
######
oiii_doublet = p1.sdss_line.OIII_5007_FLUX + p1.sdss_line.OIII_4959_FLUX
x = np.log10(oiii_doublet / p1.sdss_line.H_GAMMA_FLUX )
y = np.log10(p1.sdss_line.OIII_4363_FLUX / oiii_doublet )
y = np.log10(p1.sdss_line.NEIII_3869_FLUX / oiii_doublet )
y = np.log10(p1.sdss_line.NEIII_3869_FLUX / p1.sdss_line.H_GAMMA_FLUX )
#### tail of objects in the plot, NeIII / H g
yb = [-0.11, 0.64]
xb = [0.81, 1.53]
yi = np.interp(x, xb, yb)
box = (y < (yi-0.2)) & (x > 0.8)
plt.plot(x[box], y[box], marker='.', alpha=0.05, color='green')
plt.plot(x[sel], y[sel], marker='.', alpha=0.05)
plt.plot(x[gal_lines], y[gal_lines], marker='.', alpha=0.05)
plt.plot(x[agn], y[agn], marker='.', alpha=0.05, color='red')
plt.plot(x[sfg], y[sfg], marker='.', alpha=0.05, color='blue')
#plt.plot(x[qui], y[qui], ',', alpha=0.05, color='orange')
#plt.plot(np.log10(4.125e-15/7.566e-16)*np.array([1,1]),[-2,2])
plt.plot(np.log10(4.125e-15/3.342e-16)*np.array([1,1]),[-2,2], color='green')
plt.plot([-2,2], np.log10(2.818e-16/3.342e-16)*np.array([1,1]), color='green')
#plt.plot([-2,2], np.log10(2.818e-16/4.125e-15)*np.array([1,1]), color='green')
yy = bpty_doublet
plt.plot(bptx[sfg], yy[sfg], color='blue', alpha=0.05, marker='.', linestyle='None')
plt.plot(bptx[agn], yy[agn], color='red', alpha=0.05, marker='.', linestyle='None')
plt.plot(bptx[box], yy[box], color='green', alpha=0.05, marker='.', linestyle='None')
plt.plot([-2,2], np.log10(4.125e-15/7.566e-16)*np.array([1,1]), color='green')
def testing():
"""
Make some simple plots like SSFR vs M
"""
import unicorn.paper1 as p1
sel, NOBJ = p1.sdss_selection(zmax=0.2)
#### Mass vs. sSFR
plt.plot(p1.sdss_logm.AVG[sel], p1.sdss_totspecsfr.AVG[sel], marker='.', color='red', alpha=0.01, linestyle='None')
plt.xlim(9,12)
plt.ylim(-13,-8.5)
#### Star-forming galaxies, S/N limit on line fluxes
SN_line_limit = 3
SN_lines = (p1.sdss_line.NII_6584_FLUX/p1.sdss_line.NII_6584_FLUX_ERR > SN_line_limit) & (p1.sdss_line.H_ALPHA_FLUX/p1.sdss_line.H_ALPHA_FLUX_ERR > SN_line_limit) & (p1.sdss_line.OIII_5007_FLUX/p1.sdss_line.OIII_5007_FLUX_ERR > SN_line_limit) & (p1.sdss_line.H_BETA_FLUX/p1.sdss_line.H_BETA_FLUX_ERR > SN_line_limit)
gal_lines = sel & SN_lines # & (p1.sdss_totspecsfr > -10.7)
### BPT diagram
bptx = np.log10( p1.sdss_line.NII_6584_FLUX / p1.sdss_line.H_ALPHA_FLUX )
bpty = np.log10( p1.sdss_line.OIII_5007_FLUX / p1.sdss_line.H_BETA_FLUX )
bpty_doublet = np.log10( (p1.sdss_line.OIII_5007_FLUX + p1.sdss_line.OIII_4959_FLUX ) / p1.sdss_line.H_BETA_FLUX )
# plt.plot(bptx[gal_lines], bpty[gal_lines], marker='.', color='black', alpha=0.01, linestyle='None')
hist, xedge, yedge = np.histogram2d(bptx[gal_lines], bpty[gal_lines], bins=100, range=[[-1.5,0.8], [-1.2,1.5]])
# plt.imshow(hist.transpose(), interpolation='nearest')
# plt.contour(xedge[1:], yedge[1:], hist.transpose(), [64,256,512], colors='red', alpha=1.0, linethick=2)
Vbins = [2, 4, 8, 16, 32, 64, 128, 256, 512, 4096]
values = 1.-np.arange(len(Vbins))*1./len(Vbins)
Vcolors = []
for i in range(len(Vbins)):
Vcolors.append('%f' %(values[i]))
plt.gray()
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.gray()
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
### kauffman separation between SF galaxies and AGN
xsf = np.arange(100)/100.*1.5-1.5
ysf = 0.61/(xsf-0.05)+1.3
plt.plot(xsf, ysf, color='b', alpha=0.6, linewidth=2)
#### star-forming galaxies
ysf_int = np.interp(bptx, xsf, ysf, right=-10, left=-10)
sfg = (bpty < ysf_int) & (bptx < -0.1) & gal_lines
qui = ((bpty > ysf_int) | (bptx > -0.1)) & sel
agn = (bpty > ysf_int) & gal_lines
yy = bpty
# yy = bpty_doublet
plt.plot(bptx[sfg], yy[sfg], color='blue', alpha=0.01, marker='.', linestyle='None')
plt.plot(bptx[agn], yy[agn], color='red', alpha=0.01, marker='.', linestyle='None')
plt.xlim(-1.5, 0.8)
plt.ylim(-1.2, 1.5)
plt.xlabel('[NII]6584 / H'+r'$\alpha$')
plt.ylabel('[OIII]5007 / H'+r'$\beta$')
#### Mass vs. sSFR, for SFGs and AGN
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sel], p1.sdss_totspecsfr.AVG[sel], bins=100, range=[[9,12], [-13, -8.5]])
plt.gray()
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.gray()
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.plot(p1.sdss_logm.AVG[sfg], p1.sdss_totspecsfr.AVG[sfg], marker='.', color='blue', alpha=0.01, linestyle='None')
plt.plot(p1.sdss_logm.AVG[agn], p1.sdss_totspecsfr.AVG[agn], marker='.', color='red', alpha=0.01, linestyle='None')
plt.xlim(9,12)
plt.ylim(-13,-8.5)
plt.xlabel(r'$\log\ M/M_\odot$')
plt.ylabel('sSFR')
#### Mass vs. H-a eqw
#plt.plot(p1.sdss_logm.AVG[sel], p1.sdss_line.H_ALPHA_EQW[sel], marker='.', color='black', alpha=0.01, linestyle='None')
### log
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sel], np.log10(-p1.sdss_line.H_ALPHA_EQW[sel]), bins=100, range=[[9,11.6], [-1, 2.7]])
plt.contourf(xedge[1:], 10**yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.semilogy()
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sel], -p1.sdss_line.H_ALPHA_EQW[sel], bins=100, range=[[9,12], [-10, 80]])
plt.gray()
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.gray()
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.plot(p1.sdss_logm.AVG[sfg], p1.sdss_line.H_ALPHA_EQW[sfg], marker='.', color='blue', alpha=0.01, linestyle='None')
plt.plot(p1.sdss_logm.AVG[agn], p1.sdss_line.H_ALPHA_EQW[agn], marker='.', color='red', alpha=0.01, linestyle='None')
plt.xlim(9,12)
plt.ylim(-80, 10)
plt.xlabel(r'$\log\ M/M_\odot$')
plt.ylabel(r'$\mathrm{EQW\ H}\alpha$')
### test colors
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sel], p1.sdss_line.H_ALPHA_EQW[sel], bins=100, range=[[9,12], [-80, 10]])
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=gen_rgb_colortable(Vbins=Vbins, rgb=(1,1,1), reverse=True), alpha=1.0, linethick=2)
NBIN = 25
Vbins = [2, 4, 8, 16, 32, 64, 128, 256, 512, 4096]
Vbins = list(np.array(Vbins)*100/NBIN)
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[agn], p1.sdss_line.H_ALPHA_EQW[agn], bins=NBIN, range=[[9,12], [-80, 10]])
#plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=gen_rgb_colortable(Vbins=Vbins, rgb=(1,0,0), reverse=True), alpha=0.6, linethick=2)
plt.contour(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors='red', alpha=0.6, linethick=2)
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sfg], p1.sdss_line.H_ALPHA_EQW[sfg], bins=NBIN, range=[[9,12], [-80, 10]])
#plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=gen_rgb_colortable(Vbins=Vbins, rgb=(0,0,1), reverse=True), alpha=0.6, linethick=2)
plt.contour(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors='blue', alpha=0.6, linethick=2)
#### Eq-w vs sSFR
plt.plot(p1.sdss_line.H_ALPHA_EQW[sel], p1.sdss_totspecsfr.AVG[sel], marker='.', color='black', alpha=0.01, linestyle='None')
plt.xlim(-80, 10)
plt.ylim(-13, -8.5)
plt.ylabel(r'$\mathrm{EQW\ H}\alpha$')
plt.xlabel(r'$\mathrm{sSFR}$')
##### Check OIII 4959/5007 line ratios for SF galaxies
sfg_weak_oiii = sfg & (p1.sdss_line.OIII_4959_FLUX/p1.sdss_line.OIII_4959_FLUX_ERR > SN_line_limit)
plt.plot(np.log10(p1.sdss_line.OIII_5007_FLUX[sfg_weak_oiii]), (p1.sdss_line.OIII_4959_FLUX/p1.sdss_line.OIII_5007_FLUX)[sfg_weak_oiii], marker='.', linestyle='None', alpha=0.01, color='black')
avg_ratio = threedhst.utils.biweight((p1.sdss_line.OIII_4959_FLUX/p1.sdss_line.OIII_5007_FLUX)[sfg_weak_oiii & (np.log10(p1.sdss_line.OIII_5007_FLUX) > 1.5)], mean=True)
plt.plot([0,10],[1./2.98,1./2.98], color='red', linewidth=2)
plt.ylim(-0.1,1)
plt.xlim(0,4)
##### Case B H-a/H-b
plt.plot(np.log10(p1.sdss_line.H_ALPHA_FLUX[sfg]), (p1.sdss_line.H_BETA_FLUX/p1.sdss_line.H_ALPHA_FLUX)[sfg], marker='.', linestyle='None', alpha=0.01, color='black')
plt.plot([0,10],[1./2.86,1./2.86], color='red', linewidth=2)
plt.ylim(-0.1,1)
plt.xlim(0,4)
##### Mass vs sersic n
plt.plot(p1.sdss_logm.AVG[sfg], p1.sdss_sersic.SERSIC_N[sfg,3], marker='.', color='blue', alpha=0.01, linestyle='None')
plt.plot(p1.sdss_logm.AVG[qui], p1.sdss_sersic.SERSIC_N[qui,3], marker='.', color='red', alpha=0.01, linestyle='None')
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sel], p1.sdss_sersic.SERSIC_N[sel, 3], bins=100, range=[[9,12], [0, 6]])
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.xlabel(r'$\log\ M/M_\odot$')
plt.ylabel(r'$\mathrm{Sersic}\ n$')
plt.xlim(9,12)
plt.ylim(0,6)
##### Mass vs size
plt.plot(p1.sdss_logm.AVG[sfg], (p1.sdss_sersic.SERSIC_R50_i_KPC[sfg]), marker='.', color='blue', alpha=0.01, linestyle='None')
plt.plot(p1.sdss_logm.AVG[qui], (p1.sdss_sersic.SERSIC_R50_i_KPC[qui]), marker='.', color='red', alpha=0.01, linestyle='None')
hist, xedge, yedge = np.histogram2d(p1.sdss_logm.AVG[sel], np.log10(p1.sdss_sersic.SERSIC_R50[sel, 3]), bins=100, range=[[9,12], [-0.3, 1]])
plt.contourf(xedge[1:], yedge[1:], hist.transpose(), Vbins, colors=Vcolors, alpha=1.0, linethick=2)
plt.xlabel(r'$\log\ M/M_\odot$')
plt.ylabel(r'$R_{50}$')
plt.xlim(9,12)
plt.ylim(0,6)
def gen_rgb_colortable(Vbins = range(10), rgb = (1,0,0), reverse=False):
values = np.arange(len(Vbins))*1./len(Vbins)
if reverse:
values = 1.-values
Vcolors = []
for i in range(len(Vbins)):
Vcolors.append((rgb[0]*values[i], rgb[1]*values[i], rgb[2]*values[i]))
#
return Vcolors
def line_ratios():
"""
Get distribution of line ratios with respect to H-a to perhaps use as a prior
in fitting codes.
"""
import unicorn.paper1 as p1
import unicorn
import threedhst
import numpy as np
import matplotlib.pyplot as plt
sel, NOBJ = p1.sdss_selection(zmax=0.2)
plt.hist(np.log10(p1.sdss_line.NII_6584_FLUX[sel] / p1.sdss_line.H_ALPHA_FLUX[sel]), bins=100, range=(-2,2))
plt.hist(np.log10(p1.sdss_line.H_BETA_FLUX[sel] / p1.sdss_line.H_ALPHA_FLUX[sel]), bins=100, range=(-3,3))
plt.hist(np.log10(p1.sdss_line.H_BETA_FLUX[sel] / p1.sdss_line.OIII_5007_FLUX[sel]), bins=100, range=(-3,3))