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))
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))
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 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 = 1.0 - np.arange(len(Vbins)) * 1.0 / 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) / 100.0 * 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], [1.0 / 2.98, 1.0 / 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], [1.0 / 2.86, 1.0 / 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 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) / 100.0 * 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")
