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Libraries





In [1]:



    


import numpy              as np
import pandas             as pd
import matplotlib.pyplot  as plt
import matplotlib         as mt
import seaborn            as sns
from matplotlib.gridspec  import GridSpec
from mpl_toolkits.mplot3d import Axes3D
from __future__           import unicode_literals
from astropy.cosmology    import FlatLambdaCDM



    











In [2]:



    


# %matplotlib notebook

Loading Dataset





In [3]:



    


my_data = np.loadtxt('../Catalogue/Match07_smalldoubleclean_emlines.csv', delimiter=',', dtype=str)



    











In [4]:



    


my_dictionary = {}
for i in range(len(my_data[0, :])):                                         # Converting numpy array into dictionary
    my_dictionary[my_data[0, i]] = np.array(my_data[0 + 1:, i], dtype=str)



    











In [5]:



    


print my_data.shape
print my_data.size
print my_data[0,:]



    


















    






(14332, 164)
2350448
['CATAID' 'OBJID_SDSSDR7' 'ALPHA_J2000' 'DELTA_J2000' 'KRON_RADIUS'
 'PETRO_RADIUS' 'MAG_PETRO_u' 'MAGERR_PETRO_u' 'FLAGS_u' 'MAG_PETRO_g'
 'MAGERR_PETRO_g' 'FLAGS_g' 'MAG_PETRO_r' 'MAGERR_PETRO_r' 'FLAGS_r'
 'MAG_PETRO_i' 'MAGERR_PETRO_i' 'FLAGS_i' 'MAG_PETRO_z' 'MAGERR_PETRO_z'
 'FLAGS_z' 'MAG_PETRO_X' 'MAGERR_PETRO_X' 'FLAGS_X' 'MAG_PETRO_Y'
 'MAGERR_PETRO_Y' 'FLAGS_Y' 'MAG_PETRO_J' 'MAGERR_PETRO_J' 'FLAGS_J'
 'MAG_PETRO_H' 'MAGERR_PETRO_H' 'FLAGS_H' 'MAG_PETRO_K' 'MAGERR_PETRO_K'
 'FLAGS_K' 'FLAGS' 'PSFMAG_R' 'FIBERMAG_R' 'FLAGS_R_SDSS' 'PETRORAD_R'
 'PETROR90_R' 'PETROR50_R' 'PETROMAG_R' 'MODELMAG_U' 'MODELMAG_G'
 'MODELMAG_R' 'MODELMAG_I' 'MODELMAG_Z' 'STATUS' 'BEST_MAG_NUV'
 'BEST_MAGERR_NUV' 'BEST_MAG_FUV' 'BEST_MAGERR_FUV' 'BEST_METHOD'
 'NMATCHUV' 'NMATCHOPT' 'NUVFLAG' 'FUVFLAG' 'NN_DIST' 'NN_NMATCH4'
 'NN_MANY2ONE' 'NN_SFLAGS_NUV' 'NN_SFLAGS_FUV' 'KCORR_FUV' 'KCORR_NUV'
 'KCORR_U' 'KCORR_G' 'KCORR_R' 'KCORR_I' 'KCORR_Z' 'KCORR_Y' 'KCORR_J'
 'KCORR_H' 'KCORR_K' 'CHI2' 'MASS' 'INTSFH' 'METS' 'B300' 'B1000'
 'SURVEY_CODE' 'SURVEY_CLASS' 'nbands' 'S2N' 'PPP' 'logmstar' 'dellogmstar'
 'logmoverl_i' 'dellogmoverl_i' 'logage' 'dellogage' 'logtau' 'dellogtau'
 'logmintsfh' 'dellogmintsfh' 'logmremnants' 'dellogmremnants' 'metal'
 'delmetal' 'extBV' 'delextBV' 'logLWage' 'dellogLWage' 'gminusi'
 'delgminusi' 'uminusr' 'deluminusr' 'gminusi_stars' 'uminusr_stars'
 'C_logM_ur' 'C_logM_gi' 'C_logM_eBV' 'SPECID' 'SURVEY_CODE_EMLINES' 'SN'
 'D4000N' 'D4000N_ERR' 'SURVEY' 'Z' 'NQ' 'PROB' 'HB_FLUX_COMP'
 'HB_FLUX_ERR_COMP' 'HB_EW_COMP' 'HB_EW_ERR_COMP' 'OIIIR_FLUX_COMP'
 'OIIIR_FLUX_ERR_COMP' 'OIIIR_EW_COMP' 'OIIIR_EW_ERR_COMP' 'HA_FLUX_COMP'
 'HA_FLUX_ERR_COMP' 'HA_EW_COMP' 'HA_EW_ERR_COMP' 'NIIR_FLUX_COMP'
 'NIIR_FLUX_ERR_COMP' 'NIIR_EW_COMP' 'NIIR_EW_ERR_COMP' 'MAG_AB_FUV'
 'MAG_AB_NUV' 'MAG_AB_U' 'MAG_AB_G' 'MAG_AB_R' 'MAG_AB_I' 'MAG_AB_Z'
 'MAG_AB_Y' 'MAG_AB_J' 'MAG_AB_H' 'MAG_AB_K' 'MAG_ABSOLUTE_FUV'
 'MAG_ABSOLUTE_NUV' 'MAG_ABSOLUTE_U' 'MAG_ABSOLUTE_G' 'MAG_ABSOLUTE_R'
 'MAG_ABSOLUTE_I' 'MAG_ABSOLUTE_Z' 'MAG_ABSOLUTE_Y' 'MAG_ABSOLUTE_J'
 'MAG_ABSOLUTE_H' 'MAG_ABSOLUTE_K' 'UV_CLASS_YI2011' 'TYPE' 'BPT_CLASS'
 'WHAN_CLASS']

















In [6]:



    


redshift     = my_dictionary['Z'].astype(float)
cataid       = my_dictionary['CATAID'].astype(str)
fuv_band     = my_dictionary['MAG_AB_FUV'].astype(float)   
nuv_band     = my_dictionary['MAG_AB_NUV'].astype(float)
u_band       = my_dictionary['MAG_AB_U'].astype(float)
g_band       = my_dictionary['MAG_AB_G'].astype(float)
r_band       = my_dictionary['MAG_AB_R'].astype(float)
mag_abs_r    = my_dictionary['MAG_ABSOLUTE_R'].astype(float)
mag_abs_nuv  = my_dictionary['MAG_ABSOLUTE_NUV'].astype(float)
mag_abs_fuv  = my_dictionary['MAG_ABSOLUTE_FUV'].astype(float)
stellar_mass = my_dictionary['logmstar'].astype(float)   # stellar mass from sed fitting - log scale
stellar_age  = my_dictionary['logage'].astype(float)     # stellar age - log scale
stellar_met  = my_dictionary['metal'].astype(float)      # stellar metallicity
dn4000       = my_dictionary['D4000N'].astype(float)
h_alpha_flux = my_dictionary['HA_FLUX_COMP'].astype(float) # taken from the COMPLEX table
survey       = my_dictionary['SURVEY'].astype(str)
uv_class     = my_dictionary['UV_CLASS_YI2011'].astype(str)
bpt_class    = my_dictionary['BPT_CLASS'].astype(str)
whan_class   = my_dictionary['WHAN_CLASS'].astype(str)



    











In [7]:



    


idx_sf = np.where(whan_class=='SF')
idx_rp = np.where(whan_class=='Retired/Passive')
idx_wa = np.where(whan_class=='wAGN')
idx_sa = np.where(whan_class=='sAGN')
idx_na = np.where(whan_class=='NA')

Selecting only SDSS and GAMA observed H$\alpha$ fluxes

See warning here: http://www.gama-survey.org/dr3/schema/dmu.php?id=8

Warnings

  • Only AAOmega and SDSS spectra are flux calibrated. DO NOT USE THE LINE FLUXES DERIVED FOR SPECTRA FROM SURVEYS OTHER THAN SDSS AND GAMA!

  • SDSS spectra have been scaled by the ratio of the petrosian to 3" aperture flux. This places them on a similar absolute flux scale cf. the GAMA AAOmega spectra (although note that BOSS spectra are taken through 2" apertures so the absolute flux calibration for BOSS spectra will be systematically underestimated). The FSCALE parameter gives the scaling factor used.

  • Again, only EWs should be used for 2dFGRS, 2QZ, WiggleZ, MGC, 2SLAQ spectra.





In [8]:



    


survey_safe_idx = []
for i in range (survey.size):
    if survey[i] == 'GAMA':
        survey_safe_idx.append(i)
    elif survey[i] == 'SDSS':
        survey_safe_idx.append(i)
    else:
        continue
survey_safe_idx = np.array(survey_safe_idx)
survey_safe = survey[survey_safe_idx]

print np.unique(survey_safe)
print survey_safe.size
print survey.size
print np.unique(survey)



    


















    






['GAMA']
14331
14331
['GAMA']

SUCCESS

ALL OF OUR EMISSION LINES WERE OBSERVED BY GAMA! YES!

SFR -- See Kenicutt 1998

Calculating the luminosity for a given cosmology for H$\alpha$ fluxes





In [9]:



    


adopted_cosmology = FlatLambdaCDM(H0=70, Om0=0.3)



    











In [10]:



    


luminosity_distance = adopted_cosmology.luminosity_distance(redshift).value



    











In [11]:



    


adopted_cosmology.luminosity_distance(redshift)



    


















    
Out[11]:







$[1007.0937,~405.36982,~891.82435,~\dots, 883.42906,~1293.7449,~356.51212] \; \mathrm{Mpc}$

But... 1Mpc = 3.086E24 cm





In [12]:



    


mpc_to_cm = 3.086E24



    











In [13]:



    


h_alpha_wl        = 6.563E-5   # in cm = 6563A
idx_clean         = np.where((h_alpha_flux!=-99999.0)*(stellar_mass!=-99.))
h_alpha_clean     = h_alpha_flux[idx_clean]
uv_clean          = uv_class[idx_clean]
mass_clean        = stellar_mass[idx_clean]
stellar_age_clean = stellar_age[idx_clean]
stellar_met_clean = stellar_met[idx_clean]
dn4000_clean      = dn4000[idx_clean]



    











In [14]:



    


idx_d4k_clean = np.where((dn4000_clean>=0)*(dn4000_clean<3))
print idx_d4k_clean



    


















    






(array([    0,     1,     2, ..., 13315, 13316, 13317]),)

















In [15]:



    


# plt.hist(dn4000_clean, bins=50)
plt.hist(dn4000_clean[idx_d4k_clean], bins=50)
plt.yscale('log')
plt.show()



    


















    
























In [16]:



    


luminosity = 4*np.pi*((luminosity_distance[idx_clean]/h_alpha_wl)**2)*(h_alpha_clean)



    











In [17]:



    


luminosity_cm = luminosity * mpc_to_cm



    











In [18]:



    


sfr = (7.9E-42) * luminosity_cm  # the wl correction 
print sfr.size
print (h_alpha_flux[[stellar_mass==-99]]).size



    


















    






13318
13

















In [19]:



    


idx_uvup   = np.where(uv_class[idx_clean]=='UV_UPTURN')
idx_uvwk   = np.where(uv_class[idx_clean]=='UV_WEAK')
idx_rsf    = np.where(uv_class[idx_clean]=='RSF')
idx_redseq = np.where((nuv_band-r_band)[idx_clean]>5.4)



    











In [20]:



    


print np.array(idx_uvup).size
print np.array(idx_uvwk).size
print np.array(idx_redseq).size
print np.array(idx_uvup).size+np.array(idx_uvwk).size



    


















    






209
292
501
501

But let's separate the UV upturn objects





In [21]:



    


plt.hist(sfr, bins=50)
plt.xscale('log')
plt.show()



    


















    
























In [22]:



    


sns.set_style("white")
plt.semilogy(mass_clean[[uv_clean=='RSF']], sfr[[uv_clean=='RSF']], 'o', c='green', label='RSF', alpha=0.5)
plt.semilogy(mass_clean[[uv_clean=='UV_WEAK']], sfr[[uv_clean=='UV_WEAK']], 'o', color='yellow', label='UV weak', 
             alpha=0.5)
plt.semilogy(mass_clean[[uv_clean=='UV_UPTURN']], sfr[[uv_clean=='UV_UPTURN']], 'o', color='red', label='UV upturn', 
             alpha=0.5)
plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.legend(loc='best')
plt.ylim(10**(-3), )
plt.show()

plt.savefig('./../Figs/g2_mass_sfr.png')



    


















    
























In [23]:



    


mag_abs_nuv_clean = mag_abs_nuv[idx_clean]
mag_abs_fuv_clean = mag_abs_fuv[idx_clean]
uv_color_clean = (fuv_band-nuv_band)[idx_clean]
uv_class_clean = uv_class[idx_clean]



    











In [24]:



    


with plt.style.context("seaborn-white"):
    plt.rcParams["axes.edgecolor"] = "0.15"
    plt.rcParams["axes.linewidth"] = 1.
      
    fig = plt.figure(figsize=(8,6))
    gs  = GridSpec(4,4, height_ratios=[2.,1.5,3.,3.], width_ratios=[3.,1.5,3.,1.5])

    ax_joint  = fig.add_subplot(gs[1:4,0:3])
    ax_marg_x = fig.add_subplot(gs[0,0:3])
    ax_marg_y = fig.add_subplot(gs[1:4,3])
    
    # Scatter plot -- MAIN PLOT ------------------------------------------------------------------------------------
    ax_joint.scatter(mag_abs_nuv_clean, uv_color_clean, c='gray', alpha=0.3, label='All galaxies')
    ax_joint.scatter(mag_abs_nuv_clean[idx_uvwk], uv_color_clean[idx_uvwk], c='#dfc27d', alpha=0.5, label='UV Weak')
    ax_joint.scatter(mag_abs_nuv_clean[idx_uvup], uv_color_clean[idx_uvup], c='#a6611a', alpha=0.5, label='UV Upturn')
    ax_joint.xaxis.set_major_locator(mt.ticker.MultipleLocator(1))
    ax_joint.yaxis.set_major_locator(mt.ticker.MultipleLocator(1))
    ax_joint.tick_params('both', labelsize='14')
    l1 = ax_joint.legend(loc='lower left', numpoints=1, fontsize=14, frameon=True, framealpha=0.85)
    l1.get_frame().set_edgecolor('black')
    
    # Mnuv distribution -- on the top ------------------------------------------------------------------------------
    n, bins, patches = ax_marg_x.hist(mag_abs_nuv_clean, color='gray', alpha=0.3, bins=30, edgecolor='black', 
                                      normed=True)
    n, bins, patches = ax_marg_x.hist(mag_abs_nuv_clean[idx_uvwk], color='#dfc27d', alpha=0.5, bins=bins, 
                                      edgecolor='black', normed=True)
    n, bins, patches = ax_marg_x.hist(mag_abs_nuv_clean[idx_uvup], color='#a6611a', alpha=0.5, bins=bins, 
                                      edgecolor='black', normed=True)
    ax_marg_x.tick_params('both', labelsize='14')
    ax_marg_x.yaxis.set_major_locator(mt.ticker.MultipleLocator(0.5))
   
    
    # FUV-NUV distribution --on the right --------------------------------------------------------------------------
    n, bins, patches = ax_marg_y.hist(uv_color_clean, orientation="horizontal", color='gray', alpha=0.3, bins=20, 
                                      edgecolor='black', normed=True)
    n, bins, patches = ax_marg_y.hist(uv_color_clean[idx_uvwk], orientation="horizontal", color='#dfc27d', 
                                      alpha=0.5, bins=bins, edgecolor='black', normed=True)
    n, bins, patches = ax_marg_y.hist(uv_color_clean[idx_uvup], orientation="horizontal", color='#a6611a', 
                                      alpha=0.5, bins=bins, edgecolor='black', normed=True)
    ax_marg_y.yaxis.set_major_locator(mt.ticker.MultipleLocator(0.5))
    ax_marg_y.tick_params(axis='x', labelsize='14')
    
   # Turn off tick labels on marginals
    plt.setp(ax_marg_x.get_xticklabels(), visible=False)
    plt.setp(ax_marg_y.get_yticklabels(), visible=False)

    # Set labels on joint
    ax_joint.set_xlabel("M$_{NUV}$", fontsize=15)
    ax_joint.set_ylabel("FUV-NUV", fontsize=15)

    # Set labels on marginals
    ax_marg_y.set_xlabel('FUV-NUV \n frequency', fontsize=15)
    ax_marg_x.set_ylabel('M$_{NUV}$ \n frequency', fontsize=15)
    
    plt.savefig('../Figs/g2_cmag2_uv_dist.pdf')
    plt.savefig('../Figs/g2_cmag2_uv_dist.png')
    plt.tight_layout()
    plt.show()



    


















    








<matplotlib.figure.Figure at 0x7ff084361250>










    
























In [24]:



    


with plt.style.context("seaborn-white"):
    plt.rcParams["axes.edgecolor"] = "0.15"
    plt.rcParams["axes.linewidth"] = 1.
      
    fig = plt.figure(figsize=(8,6))
    gs  = GridSpec(4,4, height_ratios=[2.,1.5,3.,3.], width_ratios=[3.,1.5,3.,1.5])

    ax_joint  = fig.add_subplot(gs[1:4,0:3])
    ax_marg_x = fig.add_subplot(gs[0,0:3])
    ax_marg_y = fig.add_subplot(gs[1:4,3])
    
    # Scatter plot -- MAIN PLOT ------------------------------------------------------------------------------------
#     ax_joint.scatter(mag_abs_nuv_clean, uv_color_clean, c='gray', alpha=0.3, label='All galaxies')
    ax_joint.scatter(mag_abs_nuv_clean[idx_uvwk], uv_color_clean[idx_uvwk], c='#dfc27d', alpha=0.5, label='UV Weak')
    ax_joint.scatter(mag_abs_nuv_clean[idx_uvup], uv_color_clean[idx_uvup], c='#a6611a', alpha=0.5, label='UV Upturn')
    ax_joint.xaxis.set_major_locator(mt.ticker.MultipleLocator(1))
    ax_joint.yaxis.set_major_locator(mt.ticker.MultipleLocator(1))
    ax_joint.tick_params('both', labelsize='14')
    l1 = ax_joint.legend(loc='lower left', numpoints=1, fontsize=14, frameon=True, framealpha=0.85)
    l1.get_frame().set_edgecolor('black')
    
    # Mnuv distribution -- on the top ------------------------------------------------------------------------------
    n, bins, patches = ax_marg_x.hist(mag_abs_nuv_clean, color='gray', alpha=0.0, bins=30, edgecolor='black', 
                                      normed=True)
    n, bins, patches = ax_marg_x.hist(mag_abs_nuv_clean[idx_uvwk], color='#dfc27d', alpha=0.5, bins=bins, 
                                      edgecolor='black', normed=True)
    n, bins, patches = ax_marg_x.hist(mag_abs_nuv_clean[idx_uvup], color='#a6611a', alpha=0.5, bins=bins, 
                                      edgecolor='black', normed=True)
    ax_marg_x.tick_params('both', labelsize='14')
    ax_marg_x.yaxis.set_major_locator(mt.ticker.MultipleLocator(0.5))
   
    
    # FUV-NUV distribution --on the right --------------------------------------------------------------------------
    n, bins, patches = ax_marg_y.hist(uv_color_clean, orientation="horizontal", color='gray', alpha=0.0, bins=20, 
                                      edgecolor='black', normed=True)
    n, bins, patches = ax_marg_y.hist(uv_color_clean[idx_uvwk], orientation="horizontal", color='#dfc27d', 
                                      alpha=0.5, bins=bins, edgecolor='black', normed=True)
    n, bins, patches = ax_marg_y.hist(uv_color_clean[idx_uvup], orientation="horizontal", color='#a6611a', 
                                      alpha=0.5, bins=bins, edgecolor='black', normed=True)
    ax_marg_y.yaxis.set_major_locator(mt.ticker.MultipleLocator(0.5))
    ax_marg_y.tick_params(axis='x', labelsize='14')
    
   # Turn off tick labels on marginals
    plt.setp(ax_marg_x.get_xticklabels(), visible=False)
    plt.setp(ax_marg_y.get_yticklabels(), visible=False)

    # Set labels on joint
    ax_joint.set_xlabel("M$_{NUV}$", fontsize=15)
    ax_joint.set_ylabel("FUV-NUV", fontsize=15)

    # Set labels on marginals
    ax_marg_y.set_xlabel('FUV-NUV \n frequency', fontsize=15)
    ax_marg_x.set_ylabel('M$_{NUV}$ \n frequency', fontsize=15)
    
    plt.savefig('../Figs/g2_cmag3_uv_dist.pdf')
    plt.savefig('../Figs/g2_cmag3_uv_dist.png')
    plt.tight_layout()
    plt.show()



    


















    








<matplotlib.figure.Figure at 0x7f77be17c150>










    
























In [26]:



    


with plt.style.context("seaborn-white"):
    plt.rcParams["axes.edgecolor"] = "0.15"
    plt.rcParams["axes.linewidth"] = 1.
      
    fig = plt.figure(figsize=(8,6))
    gs  = GridSpec(4,4, height_ratios=[2.,1.5,3.,3.], width_ratios=[3.,1.5,3.,1.5])

    ax_joint  = fig.add_subplot(gs[1:4,0:3])
    ax_marg_x = fig.add_subplot(gs[0,0:3])
    ax_marg_y = fig.add_subplot(gs[1:4,3])
    
    # Scatter plot -- MAIN PLOT ------------------------------------------------------------------------------------
#     ax_joint.scatter(mag_abs_fuv_clean, uv_color_clean, c='gray', alpha=0.3, label='All galaxies')
    ax_joint.scatter(mag_abs_fuv_clean[idx_uvwk], uv_color_clean[idx_uvwk], c='#dfc27d', alpha=0.5, label='UV Weak')
    ax_joint.scatter(mag_abs_fuv_clean[idx_uvup], uv_color_clean[idx_uvup], c='#a6611a', alpha=0.5, label='UV Upturn')
    ax_joint.xaxis.set_major_locator(mt.ticker.MultipleLocator(1))
    ax_joint.yaxis.set_major_locator(mt.ticker.MultipleLocator(1))
    ax_joint.tick_params('both', labelsize='14')
    l1 = ax_joint.legend(loc='lower left', numpoints=1, fontsize=14, frameon=True, framealpha=0.85)
    l1.get_frame().set_edgecolor('black')
    
    # Mnuv distribution -- on the top ------------------------------------------------------------------------------
    n, bins, patches = ax_marg_x.hist(mag_abs_fuv_clean, color='gray', alpha=0.0, bins=30, edgecolor='black', 
                                      normed=True)
    n, bins, patches = ax_marg_x.hist(mag_abs_fuv_clean[idx_uvwk], color='#dfc27d', alpha=0.5, bins=bins, 
                                      edgecolor='black', normed=True)
    n, bins, patches = ax_marg_x.hist(mag_abs_fuv_clean[idx_uvup], color='#a6611a', alpha=0.5, bins=bins, 
                                      edgecolor='black', normed=True)
    ax_marg_x.tick_params('both', labelsize='14')
    ax_marg_x.yaxis.set_major_locator(mt.ticker.MultipleLocator(0.5))
   
    
    # FUV-NUV distribution --on the right --------------------------------------------------------------------------
    n, bins, patches = ax_marg_y.hist(uv_color_clean, orientation="horizontal", color='gray', alpha=0.0, bins=20, 
                                      edgecolor='black', normed=True)
    n, bins, patches = ax_marg_y.hist(uv_color_clean[idx_uvwk], orientation="horizontal", color='#dfc27d', 
                                      alpha=0.5, bins=bins, edgecolor='black', normed=True)
    n, bins, patches = ax_marg_y.hist(uv_color_clean[idx_uvup], orientation="horizontal", color='#a6611a', 
                                      alpha=0.5, bins=bins, edgecolor='black', normed=True)
    ax_marg_y.yaxis.set_major_locator(mt.ticker.MultipleLocator(0.5))
    ax_marg_y.tick_params(axis='x', labelsize='14')
    
   # Turn off tick labels on marginals
    plt.setp(ax_marg_x.get_xticklabels(), visible=False)
    plt.setp(ax_marg_y.get_yticklabels(), visible=False)

    # Set labels on joint
    ax_joint.set_xlabel("M$_{FUV}$", fontsize=15)
    ax_joint.set_ylabel("FUV-NUV", fontsize=15)

    # Set labels on marginals
    ax_marg_y.set_xlabel('FUV-NUV \n frequency', fontsize=15)
    ax_marg_x.set_ylabel('M$_{FUV}$ \n frequency', fontsize=15)
    
    plt.savefig('../Figs/g2_cmag4_uv_dist.pdf')
    plt.savefig('../Figs/g2_cmag4_uv_dist.png')
    plt.tight_layout()
    plt.show()

Redoing the above with "my" classification





In [25]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3))

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.semilogy(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: SF")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(10**(-2), 10**(3))

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2)
plt.semilogy(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV upturn')
ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(10**(-2), 10**(3))

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3)
plt.semilogy(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV upturn')
ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(M$_*$)', fontsize=15)
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(10**(-2), 10**(3))
plt.legend(loc='upper left')

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4)
plt.semilogy(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV weak')
plt.semilogy(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV upturn')
plt.title("WHAN: Retired/Passive")
ax04.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(10**(-2), 10**(3))

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5)
plt.semilogy(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: NA")
ax05.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(10**(-2), 10**(3))

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_mass_sfr_whan.png')
plt.show()

Other dependencies





In [26]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3))

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.semilogy(stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: SF")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)', fontsize='14')
plt.tick_params('both', labelsize='12')
plt.xticks([9,9.5,10])
plt.xlim(8.8,10)
plt.ylim(10**(-2), 10**(3))

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2)
plt.semilogy(stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV upturn')
ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xticks([9,9.5,10])
plt.xlim(8.8,10)
plt.ylim(10**(-2), 10**(3))

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3)
plt.semilogy(stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV upturn')
ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(t)  (Gyr)', fontsize=14)
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8.8,10)
plt.xticks([9,9.5,10])
plt.ylim(10**(-2), 10**(3))
plt.legend(loc='lower left')

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4)
plt.semilogy(stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV weak')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV upturn')
plt.title("WHAN: Retired/Passive")
ax04.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xticks([9,9.5,10])
plt.xlim(8.8,10)
plt.ylim(10**(-2), 10**(3))

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5)
plt.semilogy(stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 
            sfr[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='RSF')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 
            sfr[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV weak')
plt.semilogy(stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
            sfr[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: NA")
ax05.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
plt.ylabel('SFR (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xticks([9,9.5,10])
plt.xlim(8.8,10)
plt.ylim(10**(-2), 10**(3))

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_stellar_age_sfr_whan.png')
plt.savefig('./../Figs/g2_stellar_age_sfr_whan.pdf')
plt.show()

Other mass dependencies

Dn4000





In [27]:



    


dn4000_clean2 = dn4000_clean[idx_d4k_clean]
mass_clean2 = mass_clean[idx_d4k_clean]
uv_clean2 = uv_clean[idx_d4k_clean]
whan_clean = whan_class[idx_clean]
whan_clean2 = whan_clean[idx_d4k_clean]



    











In [28]:



    


print dn4000_clean2[[dn4000_clean2<0]].size



    


















    






0

















In [29]:



    


print dn4000_clean.size, dn4000_clean2.size, dn4000_clean.size - dn4000_clean2.size



    


















    






13318 12944 374

















In [30]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3))

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.plot(mass_clean2[[(uv_clean2=='RSF')*(whan_clean2=='SF')]], 
            dn4000_clean2[[(uv_clean2=='RSF')*(whan_clean2=='SF')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='SF')]], 
            dn4000_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='SF')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='SF')]], 
            dn4000_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='SF')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: SF")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('D$_n$ 4000', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(0, 3)

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2)
plt.plot(mass_clean2[[(uv_clean2=='RSF')*(whan_clean2=='sAGN')]], 
            dn4000_clean2[[(uv_clean2=='RSF')*(whan_clean2=='sAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='sAGN')]], 
            dn4000_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='sAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='sAGN')]], 
            dn4000_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='sAGN')]], 'o', alpha=0.5, label='UV upturn')
ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('D$_n$ 4000', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(0, 3)

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3)
plt.plot(mass_clean2[[(uv_clean2=='RSF')*(whan_clean2=='wAGN')]], 
            dn4000_clean2[[(uv_clean2=='RSF')*(whan_clean2=='wAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='wAGN')]], 
            dn4000_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='wAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='wAGN')]], 
            dn4000_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='wAGN')]], 'o', alpha=0.5, label='UV upturn')
ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(M$_*$)', fontsize=14)
plt.ylabel('D$_n$ 4000', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(0, 3)
ax03.legend(loc='lower center', bbox_to_anchor=(2.1, -0.3), fontsize=12, frameon=True, framealpha=1., ncol=3)

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4)
plt.plot(mass_clean2[[(uv_clean2=='RSF')*(whan_clean2=='Retired/Passive')]], 
            dn4000_clean2[[(uv_clean2=='RSF')*(whan_clean2=='Retired/Passive')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='Retired/Passive')]], 
            dn4000_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV weak')
plt.plot(mass_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='Retired/Passive')]], 
            dn4000_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV upturn')
plt.title("WHAN: Retired/Passive")
ax04.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
plt.ylabel('D$_n$ 4000', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(0, 3)

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5)
plt.plot(mass_clean2[[(uv_clean2=='RSF')*(whan_clean2=='NA')]], 
            dn4000_clean2[[(uv_clean2=='RSF')*(whan_clean2=='NA')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='NA')]], 
            dn4000_clean2[[(uv_clean2=='UV_WEAK')*(whan_clean2=='NA')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='NA')]], 
            dn4000_clean2[[(uv_clean2=='UV_UPTURN')*(whan_clean2=='NA')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: NA")
ax05.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
plt.ylabel('D$_n$ 4000', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(0, 3)

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_mass_dn4000_whan.png')
plt.savefig('./../Figs/g2_mass_dn4000_whan.pdf')
plt.show()



    


















    
























In [31]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3))

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: SF")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('Log(t)  (Gyr)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(8.5,)

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV upturn')
# ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_age_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(9.3,)

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV upturn')
# ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(M$_*$)', fontsize=14)
# plt.ylabel('Log(t)  (Gyr)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(9.6, )
ax03.legend(loc='lower center', bbox_to_anchor=(2.3, -0.3), fontsize=12, frameon=True, framealpha=1., ncol=3)

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV upturn')
plt.title("WHAN: Retired/Passive")
# ax04.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_age_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(9.6,)

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: NA")
# ax05.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_age_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
plt.ylim(9.1, )

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_mass_stellar_age_whan_01.png')
plt.show()



    


















    
























In [34]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3), sharey=True)

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: SF")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('Log(t)  (Gyr)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(8.5,)

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV upturn')
ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_age_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.3,)

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV upturn')
ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(M$_*$)', fontsize=14)
# plt.ylabel('Log(t)  (Gyr)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.6, )
ax03.legend(loc='lower center', bbox_to_anchor=(2.1, -0.3), fontsize=12, frameon=True, framealpha=1., ncol=3)

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV upturn')
plt.title("WHAN: Retired/Passive")
ax04.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_age_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.6,)

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 
            stellar_age_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 
            stellar_age_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: NA")
ax05.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_age_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.1, )

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_mass_stellar_age_whan_02.png')
plt.show()



    


















    
























In [32]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3), sharey=True)

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 
        stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 'o',  color='#a6611a',
         alpha=0.8, label='UV upturn')
sns.kdeplot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]],
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], color='#a6611a',
            alpha=0.4, label='UV upturn')
plt.title("WHAN: SF")
plt.ylabel('Log(t)  (Gyr)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(9, 12)
plt.ylim(9.70, 9.93)

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 
        stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 'o', color='#a6611a',
         alpha=0.8, label='UV upturn')
sns.kdeplot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]],
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], color='#a6611a',
            alpha=0.4, label='UV upturn')
ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
plt.tick_params('both', labelsize='12')
plt.xlim(9, 12)
plt.ylim(9.70, 9.93)

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 
        stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 'o', color='#a6611a', 
         alpha=0.8, label='UV upturn')
sns.kdeplot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]],
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], color='#a6611a',
            alpha=0.4, label='UV upturn')
ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(M$_*$)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(9, 12)
plt.ylim(9.70, 9.93)
# ax03.legend(loc='lower center', bbox_to_anchor=(2.1, -0.3), fontsize=12, frameon=True, framealpha=0.8, ncol=1)

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]],
         stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 'o',  
         color='#a6611a', alpha=0.8,label='UV upturn')
sns.kdeplot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]],
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 
            color='#a6611a',alpha=0.4, label='UV upturn')
plt.title("WHAN: Retired/Passive")
ax04.yaxis.set_visible(False)
plt.tick_params('both', labelsize='12')
plt.xlim(9, 12)
plt.ylim(9.70, 9.93)

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
         stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 'o', color='#a6611a', 
         alpha=0.8, label='UV upturn')
sns.kdeplot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]],
            stellar_age_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
            color='#a6611a',alpha=0.4, label='UV upturn')
plt.title("WHAN: NA")
ax05.yaxis.set_visible(False)
plt.tick_params('both', labelsize='12')
plt.xlim(9, 12)
plt.ylim(9.70, 9.93)

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_mass_stellar_age_whan_uvup.png')
plt.show()



    


















    
























In [33]:



    


print stellar_age_clean[[(uv_clean=='UV_UPTURN')]].max()
print stellar_age_clean[[(uv_clean=='UV_UPTURN')]].min()



    


















    






9.915195
9.36917

















In [34]:



    


sns.set_style("white")
plt.rcParams["axes.edgecolor"] = "0.15"
plt.rcParams["axes.linewidth"] = 1.
plt.subplots(1,1, figsize=(10,3.3), sharey=True)

# WHAN SF CLASS ------------------
ax01 = plt.subplot(1,5,1)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 
            stellar_met_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 
            stellar_met_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 
            stellar_met_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='SF')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: SF")
# plt.xlabel('Log(M$_*$)')
plt.ylabel('<Z/Z$_{\odot}$>', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(8.5,)

# WHAN sAGN CLASS ------------------
ax02 = plt.subplot(1,5,2, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_met_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_met_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 
            stellar_met_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='sAGN')]], 'o', alpha=0.5, label='UV upturn')
ax02.yaxis.set_visible(False)
plt.title("WHAN: sAGN")
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_met_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.3,)

# WHAN wAGN CLASS ------------------
ax03= plt.subplot(1,5,3, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_met_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_met_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 
            stellar_met_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='wAGN')]], 'o', alpha=0.5, label='UV upturn')
ax03.yaxis.set_visible(False)
plt.title("WHAN: wAGN")
plt.xlabel('Log(M$_*$)', fontsize=14)
# plt.ylabel('Log(t)  (Gyr)', fontsize=14)
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.6, )
ax03.legend(loc='lower center', bbox_to_anchor=(2.1, -0.3), fontsize=12, frameon=True, framealpha=1., ncol=3)

# WHAN Retired/Passive CLASS ------------------
ax04 = plt.subplot(1,5,4, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_met_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_met_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 
            stellar_met_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]], 'o', alpha=0.5, 
             label='UV upturn')
plt.title("WHAN: Retired/Passive")
ax04.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_met_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.6,)

# WHAN NA CLASS (unclassified) ------------------
ax05 = plt.subplot(1,5,5, sharey=ax01)
plt.plot(mass_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 
            stellar_met_clean[[(uv_clean=='RSF')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='RSF')
plt.plot(mass_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 
            stellar_met_clean[[(uv_clean=='UV_WEAK')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV weak')
plt.plot(mass_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
            stellar_met_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 'o', alpha=0.5, label='UV upturn')
plt.title("WHAN: NA")
ax05.yaxis.set_visible(False)
# plt.xlabel('Log(M$_*$)')
# plt.ylabel('stellar_met_clean (M$_\odot$ yr$^{-1}$)')
plt.tick_params('both', labelsize='12')
plt.xlim(8, 13)
# plt.ylim(9.1, )

plt.tight_layout(w_pad=0.0)
plt.savefig('./../Figs/g2_mass_stellar_met_whan.png')
plt.show()



    


















    
























In [37]:



    


cataid_clean = cataid[idx_clean]
objdr7_clean = my_dictionary['OBJID_SDSSDR7'][idx_clean]



    











In [38]:



    


print cataid_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]], 
cataid_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]].size



    


















    






['598961' '611468' '301850' '321414' '373305' '375789' '376042' '423115'
 '3604402' '3613123' '492701' '3882902' '3888169' '3889591' '3910157'
 '39151' '39732' '48736' '55601' '65427' '70294' '549148' '562060' '138989'
 '196101' '209881' '216892' '574429']









    
Out[38]:








28










    






























In [39]:



    


print objdr7_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]],
objdr7_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]].size



    


















    






['588848900427481399' '588848900451729571' '587726032229040530'
 '587726032267378887' '587727942956024011' '587727943488635247'
 '587727943489094234' '587728879794585926' '587729150911971799'
 '587729150914134215' '587729778519507274' '588010930832998667'
 '588010930834374925' '588010930834768089' '588010931371376935'
 '587722982274957434' '587722982276071649' '587722982293766374'
 '587722982815039630' '587722982832669131' '587722983350403258'
 '588848899354132992' '588848899378446610' '587724650333012209'
 '587725073914003890' '587725074990432619' '587725075529400544'
 '588848899893297496']









    
Out[39]:








28










    






























In [40]:



    


spec_i_need = {}
spec_i_need['CATAID'] = cataid_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]]
spec_i_need['OBJID_SDSSDR7'] = objdr7_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='NA')]]



    



















In [41]:



    


cataid_uvup_rp = cataid_clean[[(uv_clean=='UV_UPTURN')*(whan_class[idx_clean]=='Retired/Passive')]]



    



















In [42]:



    


print cataid_uvup_rp.size
for i in range(cataid_uvup_rp.size):
    print cataid_uvup_rp[i]



    


















    






87
594994
600232
609836
610293
610441
617899
619222
619575
6840
14922
17387
22885
23262
23618
231048
271394
272306
273046
273844
289261
289379
297022
298594
301906
302370
318790
318810
324302
343900
361693
363924
371001
381583
383050
417755
422871
519135
3582527
3584590
3604645
3613085
3617808
3623290
3627066
460401
3878612
3909928
30716
30793
30897
31060
31799
39013
41058
47685
48688
49358
54859
54925
54979
55641
63271
84716
84945
85618
85928
93394
99005
106081
106915
534783
536903
549755
560345
560512
560599
561506
569109
143845
144624
145111
196998
198705
203412
203848
238896
240455

























In [43]:



    


spec_i_need = pd.DataFrame(spec_i_need)



    



















In [44]:



    


spec_i_need.to_csv('./spec.csv', index=False)



    



















In [45]:



    


spec_i_need



    


















    
Out[45]:










  
    

      
      CATAID
      OBJID_SDSSDR7
    

  
  
    

      0
      598961
      588848900427481399
    

    

      1
      611468
      588848900451729571
    

    

      2
      301850
      587726032229040530
    

    

      3
      321414
      587726032267378887
    

    

      4
      373305
      587727942956024011
    

    

      5
      375789
      587727943488635247
    

    

      6
      376042
      587727943489094234
    

    

      7
      423115
      587728879794585926
    

    

      8
      3604402
      587729150911971799
    

    

      9
      3613123
      587729150914134215
    

    

      10
      492701
      587729778519507274
    

    

      11
      3882902
      588010930832998667
    

    

      12
      3888169
      588010930834374925
    

    

      13
      3889591
      588010930834768089
    

    

      14
      3910157
      588010931371376935
    

    

      15
      39151
      587722982274957434
    

    

      16
      39732
      587722982276071649
    

    

      17
      48736
      587722982293766374
    

    

      18
      55601
      587722982815039630
    

    

      19
      65427
      587722982832669131
    

    

      20
      70294
      587722983350403258
    

    

      21
      549148
      588848899354132992
    

    

      22
      562060
      588848899378446610
    

    

      23
      138989
      587724650333012209
    

    

      24
      196101
      587725073914003890
    

    

      25
      209881
      587725074990432619
    

    

      26
      216892
      587725075529400544
    

    

      27
      574429
      588848899893297496
    

  




























In [ ]:



    






    
















    


    

        

            

            

                Content source: mdastro/UV_ETGs
            

            
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