def mass_distance_bin_compare():
fig = plt.figure(figsize=(6.4, 4.8))
gs = gridspec.GridSpec(1, 1)
axs0 = plt.subplot(gs[0])
max_mass = 5e13
#axs1.hlines(2e12,0,110,linestyle="solid")
axs0.hlines(5e11, 75, 110, linestyle="solid")
axs0.hlines(2e11, 45, 75, linestyle="solid")
axs0.hlines(1e11, 0, 45, linestyle="solid")
#q1 = np.log10(5e11)-np.average(E1_T11-key_delta_Bin1)-0.5*np.std(E1_T11-key_delta_Bin1)
#q2 = np.log10(2e11)-np.average(E2_T11-key_delta_Bin2)-0.5*np.std(E2_T11-key_delta_Bin2)
#q3 = np.log10(1e11)-np.average(E3_T11-key_delta_Bin3)-0.5*np.std(E3_T11-key_delta_Bin3)
#q1 = np.log10(5e11)-np.average(E1_Z09-key_delta_Bin1)-0*np.std(E1_Z09-key_delta_Bin1)
#q2 = np.log10(2e11)-np.average(E2_Z09-key_delta_Bin2)-0*np.std(E2_Z09-key_delta_Bin2)
#q3 = np.log10(1e11)-np.average(E3_Z09-key_delta_Bin3)-0*np.std(E3_Z09-key_delta_Bin3)
#q1 = np.log10(5e11)-0.45*np.average(E1_RC15-key_delta_Bin1)-0*np.std(E1_Z09-key_delta_Bin1)
#q2 = np.log10(2e11)-0.45*np.average(E2_RC15-key_delta_Bin2)-0*np.std(E2_Z09-key_delta_Bin2)
#q3 = np.log10(1e11)-0.45*np.average(E3_RC15-key_delta_Bin3)-0*np.std(E3_Z09-key_delta_Bin3)
#q1 = np.log10(5e11)-np.average(E1_T11-key_delta_Bin1)
#q2 = np.log10(2e11)-np.average(E2_T11-key_delta_Bin2)
#q3 = np.log10(1e11)-np.average(E3_T11-key_delta_Bin3)
#q1 = np.log10(5e11)-max_E1_ERC15
#q2 = np.log10(2e11)-max_E2_ERC15
#q3 = np.log10(1e11)-max_E3_ERC15
#q1 = np.log10(5e11)-max_E1_EZ09
#q2 = np.log10(2e11)-max_E2_EZ09
#q3 = np.log10(1e11)-max_E3_EZ09
q1 = np.log10(5e11) - max_E1_ET11
q2 = np.log10(2e11) - max_E2_ET11
q3 = np.log10(1e11) - max_E3_ET11
print("q1 , q2, q3", q1, q2, q3)
axs0.hlines(10**q1, 75, 110, linestyle="solid")
axs0.hlines(10**q2, 45, 75, linestyle="solid")
axs0.hlines(10**q3, 0, 45, linestyle="solid")
axs0.vlines(110, 5e11, max_mass, linestyle="solid")
axs0.vlines(75, 2e11, max_mass, linestyle="solid")
axs0.vlines(45, 1e11, max_mass, linestyle="solid")
x_edge1, y_edge1 = [75, 75, 110, 110], [5e11, max_mass, max_mass, 5e11]
x_edge2, y_edge2 = [45, 45, 75, 75], [2e11, max_mass, max_mass, 2e11]
x_edge3, y_edge3 = [0, 0, 45, 45], [1e11, max_mass, max_mass, 1e11]
axs0.plot(DD1,
E,
'x',
color='#a51a74',
alpha=0.8,
label='New Distance IP13')
axs0.plot(DD1, E_T11, 'x', color='g', alpha=0.8, label='T11')
#axs1.plot(DD1,E_Z09,'x',color='b', alpha=0.8, label='Z09')
#axs1.plot(DD1,E_RC15,'x',color='k', alpha=0.8, label='RC15')
axs0.plot(corr_dist1,
corr_E1,
'ro',
label='Bin1 samples',
ms=12,
alpha=0.8)
axs0.plot(corr_dist2,
corr_E2,
'bo',
label='Bin2 samples',
ms=12,
alpha=0.8)
axs0.plot(corr_dist3,
corr_E3,
'ko',
label='Bin3 samples',
ms=12,
alpha=0.8)
axs0.plot(corr_dist1,
10**E1_T11,
'rs',
label='Bin1 samples',
ms=12,
alpha=0.8)
axs0.plot(corr_dist2,
10**E2_T11,
'bs',
label='Bin2 samples',
ms=12,
alpha=0.8)
axs0.plot(corr_dist3,
10**E3_T11,
'ks',
label='Bin3 samples',
ms=12,
alpha=0.8)
#axs0.plot(corr_dist1,10**E1_Z09,'rs',label='Bin1 samples', ms=12,alpha=0.8)
#axs0.plot(corr_dist2,10**E2_Z09,'bs',label='Bin2 samples', ms=12,alpha=0.8)
#axs0.plot(corr_dist3,10**E3_Z09,'ks',label='Bin3 samples', ms=12,alpha=0.8)
#axs0.plot(corr_dist1,10**E1_RC15,'rs',label='Bin1 samples', ms=12,alpha=0.8)
#axs0.plot(corr_dist2,10**E2_RC15,'bs',label='Bin2 samples', ms=12,alpha=0.8)
#axs0.plot(corr_dist3,10**E3_RC15,'ks',label='Bin3 samples', ms=12,alpha=0.8)
axs0.plot(dc,
SPlot.SelectionCut(initial_mass, dc).parent_sample_cut(),
linestyle="solid",
color="blue",
linewidth=2)
axs0.vlines(115,
4.17e11,
1e15,
linestyle="solid",
color="blue",
linewidth=2)
axs0.fill(x_edge1, y_edge1, alpha=0.3, color='green')
axs0.fill(x_edge2, y_edge2, alpha=0.2, color='green')
axs0.fill(x_edge3, y_edge3, alpha=0.1, color='green')
axs0.set_ylim(top=5e12, bottom=1e9)
axs0.set_xlim(left=0, right=120)
axs0.set_ylabel(r"$ M_*/ \rm M_\odot(IP13)$", fontsize=16)
axs0.set_xlabel(r"$ \rm Distance/Mpc$", fontsize=16)
axs0.set_yscale('log')
axs0.set_ylim(top=5e13, bottom=1e10)
axs0.set_xlim(left=0, right=120)
axs0.legend(loc=4, fontsize=10)
plt.tight_layout()
plt.show()
vel_err_index=7,
scale_index=8,
dir_name_index=0,
dir_dist_index=3,
dir_dist_err_index=4,
dir_method_index=5,
dir_method_flag_index=7)
name1 = D["Gal_name"]
DD1 = D["Dist"]
DD1_err = D["Dist_err"]
scale1 = D["Scale"]
mag_g, mag_i = broad_cut[:, 10], broad_cut[:, 9]
ML_select_T11 = SPlot.MLRelationIband(mag_g, mag_i).Taylor11_MassRatio
ML_select_Z09 = SPlot.MLRelationIband(mag_g, mag_i).Zibetti09_MassRatio
ML_select_RC15 = SPlot.MLRelationIband(mag_g, mag_i).Roediger15BC03_MassRatio
ML_select_IP13 = SPlot.MLRelationIband(mag_g, mag_i).Into13_MassRatio
M = SPlot.MassCalculation(mag_i, DD1, 4.53, mag_g, mag_i)
E = M.cal_Mass(ML_select_IP13)
E_T11 = M.cal_Mass(ML_select_T11)
E_Z09 = M.cal_Mass(ML_select_Z09)
E_RC15 = M.cal_Mass(ML_select_RC15)
# same calculation but seperated by bin
ML_select1_T11 = SPlot.MLRelationIband(g1, i1).Taylor11_MassRatio
ML_select1_Z09 = SPlot.MLRelationIband(g1, i1).Zibetti09_MassRatio
ML_select1_RC15 = SPlot.MLRelationIband(g1, i1).Roediger15BC03_MassRatio
def mass_distance_2plots():
fig = plt.figure(figsize=(6.4, 9.6))
gs = gridspec.GridSpec(2, 1)
axs1 = plt.subplot(gs[1])
axs0 = plt.subplot(gs[0])
max_mass = 5e13
#axs1.hlines(2e12,0,110,linestyle="solid")
axs1.hlines(5e11, 75, 110, linestyle="solid")
axs1.hlines(2e11, 45, 75, linestyle="solid")
axs1.hlines(1e11, 0, 45, linestyle="solid")
axs1.vlines(110, 5e11, max_mass, linestyle="solid")
axs1.vlines(75, 2e11, max_mass, linestyle="solid")
axs1.vlines(45, 1e11, max_mass, linestyle="solid")
x_edge1, y_edge1 = [75, 75, 110, 110], [5e11, max_mass, max_mass, 5e11]
x_edge2, y_edge2 = [45, 45, 75, 75], [2e11, max_mass, max_mass, 2e11]
x_edge3, y_edge3 = [0, 0, 45, 45], [1e11, max_mass, max_mass, 1e11]
axs0.plot(dc, initial_mass, 'x', color='grey', alpha=0.8, label='All')
axs0.plot(dc,
SPlot.SelectionCut(initial_mass, dc).parent_sample_cut(),
linestyle="solid",
color="blue",
linewidth=2)
axs0.vlines(115,
4.17e11,
1e15,
linestyle="solid",
color="blue",
linewidth=2)
#axs0.plot(dc,E,'x',color='green', alpha=0.4, label='New Distance')
axs0.plot(dc_b,
initial_mass_b,
'x',
color='green',
alpha=0.8,
label='Broad Selection')
axs1.plot(DD1,
E,
'x',
color='#a51a74',
alpha=0.8,
label='New Distance IP13')
axs1.plot(DD1, E_T11, 'x', color='g', alpha=0.8, label='T11')
#axs1.plot(DD1,E_Z09,'x',color='b', alpha=0.8, label='Z09')
#axs1.plot(DD1,E_RC15,'x',color='k', alpha=0.8, label='RC15')
#axs1.plot(corr_dist1,corr_E1,'ro',label='Bin1 samples', ms=12,alpha=0.8)
#axs1.plot(corr_dist2,corr_E2,'bo',label='Bin2 samples', ms=12,alpha=0.8)
#axs1.plot(corr_dist3,corr_E3,'ko',label='Bin3 samples', ms=12,alpha=0.8)
axs1.plot(d1, 10**E1_d, 'ro', label='Bin1 samples', ms=12, alpha=0.8)
axs1.plot(d2, 10**E2_d, 'bo', label='Bin2 samples', ms=12, alpha=0.8)
axs1.plot(d3, 10**E3_d, 'ko', label='Bin3 samples', ms=12, alpha=0.8)
#axs1.plot(corr_dist1,10**E1_T11,'rs',label='Bin1 samples', ms=12,alpha=0.8)
#axs1.plot(corr_dist2,10**E2_T11,'bs',label='Bin2 samples', ms=12,alpha=0.8)
#axs1.plot(corr_dist3,10**E3_T11,'ks',label='Bin3 samples', ms=12,alpha=0.8)
axs1.plot(dc,
SPlot.SelectionCut(initial_mass, dc).parent_sample_cut(),
linestyle="solid",
color="blue",
linewidth=2)
axs1.vlines(115,
4.17e11,
1e15,
linestyle="solid",
color="blue",
linewidth=2)
axs1.fill(x_edge1, y_edge1, alpha=0.3, color='green')
axs1.fill(x_edge2, y_edge2, alpha=0.2, color='green')
axs1.fill(x_edge3, y_edge3, alpha=0.1, color='green')
axs0.set_ylim(top=5e12, bottom=1e9)
axs0.set_xlim(left=0, right=120)
axs0.set_ylabel(r"$ M_*/\rm M_\odot(IP13)$", fontsize=16)
axs0.set_xlabel(r"$ \rm Distance/Mpc$", fontsize=16)
axs0.set_yscale('log')
axs1.set_ylabel(r"$ M_*/ \rm M_\odot(IP13)$", fontsize=16)
axs1.set_xlabel(r"$ \rm Distance/Mpc$", fontsize=16)
axs1.set_yscale('log')
axs1.set_ylim(top=2e12, bottom=1e10)
axs1.set_xlim(left=0, right=120)
axs0.legend(loc=4, fontsize=10)
axs1.legend(loc=4, fontsize=10)
plt.tight_layout()
plt.show()
["Bulge", "CoreBulge"], 1) #get Re
Re_3 = SRead.grab_parameter("F_Gal_bundle_equvi_Bin3V_cpt",
["Bulge", "CoreBulge"], 1) #get Re
ars = (4.84814e-6) * 1e3 # 1arcsec = (4.84814e-6) rad ars:arcsec to rad scale
scale1 = D1 * ars
scale2 = D2 * ars
scale3 = D3 * ars
Re_1_kpc = Re_1 * scale1
Re_2_kpc = Re_2 * scale2
Re_3_kpc = Re_3 * scale3
# Calculate mass
ML_select1_IP13 = SPlot.MLRelationIband(mag_g1, mag_i1).Into13_MassRatio
ML_select1_R15BC = SPlot.MLRelationIband(mag_g1,
mag_i1).Roediger15BC03_MassRatio
ML_select1_Z09 = SPlot.MLRelationIband(mag_g1, mag_i1).Zibetti09_MassRatio
ML_select1_T11 = SPlot.MLRelationIband(mag_g1, mag_i1).Taylor11_MassRatio
M1 = SPlot.MassCalculation(sph_mag1, D1, 4.53, mag_g1, mag_i1)
E1_IP13 = M1.cal_Mass(ML_select1_IP13)
E1_R15BC = M1.cal_Mass(ML_select1_R15BC)
E1_Z09 = M1.cal_Mass(ML_select1_Z09)
E1_T11 = M1.cal_Mass(ML_select1_T11)
ML_select2_IP13 = SPlot.MLRelationIband(mag_g2, mag_i2).Into13_MassRatio
ML_select2_R15BC = SPlot.MLRelationIband(mag_g2,
mag_i2).Roediger15BC03_MassRatio
mag_i2 = np.array(SRead.extract_match(name2, vdis_file2[:, 0], vdis_file[:,
9]))
mag_g3 = np.array(
SRead.extract_match(name3, vdis_file2[:, 0], vdis_file[:, 10]))
mag_i3 = np.array(SRead.extract_match(name3, vdis_file2[:, 0], vdis_file[:,
9]))
mag_g4 = np.array(
SRead.extract_match(name4, vdis_file2[:, 0], vdis_file[:, 10]))
mag_i4 = np.array(SRead.extract_match(name4, vdis_file2[:, 0], vdis_file[:,
9]))
############# new distance, new i-band magnitude
ML_select2 = SPlot.MLRelationIband(mag_g2, mag_i2).Into13_MassRatio
M2 = SPlot.MassCalculation(total_mag2, DD2, 4.53, mag_g2, mag_i2)
E2 = M2.cal_Mass(ML_select2)
ML_select3 = SPlot.MLRelationIband(mag_g3, mag_i3).Into13_MassRatio
M3 = SPlot.MassCalculation(total_mag3, DD3, 4.53, mag_g3, mag_i3)
E3 = M3.cal_Mass(ML_select3)
ML_select4 = SPlot.MLRelationIband(mag_g4, mag_i4).Into13_MassRatio
M4 = SPlot.MassCalculation(total_mag4, DD4, 4.53, mag_g4, mag_i4)
E4 = M4.cal_Mass(ML_select4)
############# old distance, old i-band magnitude
M2_o1 = SPlot.MassCalculation(mag_i2, DD2, 4.53, mag_g2, mag_i2)
E2_o1 = M2_o1.cal_Mass(ML_select2)
D = SRead.grab_dist(
"/home/dexter/result/stat/completeness/dir_dist_list_broad3_2.txt",
"/home/dexter/result/stat/completeness/diagonal_selection_bag3_2.dat",
name_index = 4, vel_index = 6, vel_err_index = 7, scale_index = 8,
dir_name_index = 0, dir_dist_index = 3, dir_dist_err_index =4,
dir_method_index = 5, dir_method_flag_index = 7)
name1 = D["Gal_name"]
DD1 = D["Dist"]
DD1_err = D["Dist_err"]
scale1 = D["Scale"]
mag_g, mag_i = np.nan_to_num(broad_cut[:,10],neginf=0),np.nan_to_num(broad_cut[:,9],neginf=0)
ML_select_T11= SPlot.MLRelationIband(mag_g,mag_i).Taylor11_MassRatio
ML_select_Z09= SPlot.MLRelationIband(mag_g,mag_i).Zibetti09_MassRatio
ML_select_RC15= SPlot.MLRelationIband(mag_g,mag_i).Roediger15BC03_MassRatio
ML_select_IP13= SPlot.MLRelationIband(mag_g,mag_i).Into13_MassRatio
M = SPlot.MassCalculation(mag_i, DD1, 4.53,mag_g,mag_i)
E_IP13 = M.cal_Mass(ML_select_IP13)
E_T11 = M.cal_Mass(ML_select_T11)
E_Z09 = M.cal_Mass(ML_select_Z09)
E_RC15 = M.cal_Mass(ML_select_RC15)
from scipy.optimize import curve_fit
CCC4 = np.genfromtxt("Ctable_Into_bin4_final.txt", dtype='float')
R_e_4 = SRead.grab_parameter("Gal_bundle_equvi_bin4_cpt",
["Bulge", "CoreBulge"], 1) #get Re
mag4 = SRead.grab_mag("Gal_bundle_equvi_bin4_cpt", ["Bulge", "CoreBulge"])
AbsMag_g_bin4 = CCC4[:, 6]
AbsMag_i_bin4 = CCC4[:, 8]
#Mass_bulge_Into2 = cal_Mass(mag2,DD2,ML_relation_Iband(AbsMag_g_bin2,AbsMag_i_bin2).Into13_MassRatio,M_sun)
Re_kpc4 = R_e_4 * scale4
Mag4 = mag4 - 25 - 5 * np.log10(DD4)
Lum4 = 10**((Mag4 - (4.53)) / (-2.5))
################################
ML_select2 = SPlot.MLRelationIband(AbsMag_g_bin2,
AbsMag_i_bin2).Into13_MassRatio
M2 = SPlot.MassCalculation(mag2, DD2, M_sun, AbsMag_g_bin2, AbsMag_i_bin2)
E2 = M2.cal_Mass(ML_select2)
ML_select3 = SPlot.MLRelationIband(AbsMag_g_bin3,
AbsMag_i_bin3).Into13_MassRatio
M3 = SPlot.MassCalculation(mag3, DD3, M_sun, AbsMag_g_bin3, AbsMag_i_bin3)
E3 = M3.cal_Mass(ML_select3)
ML_select4 = SPlot.MLRelationIband(AbsMag_g_bin4,
AbsMag_i_bin4).Into13_MassRatio
M4 = SPlot.MassCalculation(mag4, DD4, M_sun, AbsMag_g_bin4, AbsMag_i_bin4)
E4 = M4.cal_Mass(ML_select4)
SPlot.ShowcaseIndi.Mass_Re_plot(E2, Re_kpc2, name2, "ro", "test", 1.0)
SPlot.ShowcaseIndi.Mass_Re_plot(E3, Re_kpc3, name3, "bo", "test", 1.0)
D, D_lerr, D_uerr = D0_all_table[:, 29], D0_all_table[:, 30], D0_all_table[:,
31]
morph = D0_all_table_n[:, -4]
elle = geom_file[:, 7] #extended disk ellipicity
# Read the data from the galaxy BD bundle for dust correction later
sph_mag_i_BD = SRead.grab_mag(bundle_BD_name, ["Bulge"])
disc_mag_i_BD = SRead.grab_mag(bundle_BD_name, ["Disk"])
Abs_sph_mag_i_BD = sph_mag_i_BD - 25 - 5 * np.log10(D)
Abs_disc_mag_i_BD = disc_mag_i_BD - 25 - 5 * np.log10(D)
# Calculate the absoulte magnitude and the stellar mass
ML_select_T11 = SPlot.MLRelationIband(mag_g_kcorr,
mag_i_kcorr).Taylor11_MassRatio
ML_select_RC15 = SPlot.MLRelationIband(mag_g_kcorr,
mag_i_kcorr).Roediger15BC03_MassRatio
ML_select_Z09 = SPlot.MLRelationIband(mag_g_kcorr,
mag_i_kcorr).Zibetti09_MassRatio
ML_select_IP13 = SPlot.MLRelationIband(mag_g_kcorr,
mag_i_kcorr).Into13_MassRatio
M = SPlot.MassCalculation(sph_mag, D, 4.53, mag_g_kcorr, mag_i_kcorr)
Abs_sph_mag = M.cal_abs_mag()
# Calculate the dust corrected version of abs mag for ALL sample
# E and S0 does not require that but I calculate them nonethesless
Abs_sph_mag_dustCorr = M.dust_correction_Driver08(Abs_sph_mag, elle)
# Calculate the dust corrected version of i-band galaxy total mag for ALL sample
vel_err_index=7,
scale_index=8,
dir_name_index=0,
dir_dist_index=3,
dir_dist_err_index=4,
dir_method_index=5,
dir_method_flag_index=7)
name1 = D["Gal_name"]
DD1 = D["Dist"]
DD1_err = D["Dist_err"]
scale1 = D["Scale"]
mag_g, mag_i = broad_cut[:, 10], broad_cut[:, 9]
ML_select = SPlot.MLRelationIband(mag_g, mag_i).Into13_MassRatio
M = SPlot.MassCalculation(mag_i, DD1, 4.53, mag_g, mag_i)
E = M.cal_Mass(ML_select)
import statistics
import matplotlib.gridspec as gridspec
fig = plt.figure()
gs = gridspec.GridSpec(2, 1)
axs1 = plt.subplot(gs[1])
axs0 = plt.subplot(gs[0])
#SPlot.ShowcaseIndi.show_name(DD1,E,name_b)
BL = SSort.vdis_match("./galaxy_bundle/Gal_bundle_equvi_cpt_LTG",
"../result/velocity_disp/vel_disp_list_all_mag.txt",
["../result/distance/dir_dist_list.txt","../result/distance/dist_list.txt"],
"./galaxy_bundle/Gal_vdis_dict_equvi_LTG")
BE = SSort.vdis_match("./galaxy_bundle/Gal_bundle_equvi_cpt_ETG",
"../result/velocity_disp/vel_disp_list_all_mag.txt",
["../result/distance/dir_dist_list.txt","../result/distance/dist_list.txt"],
"./galaxy_bundle/Gal_vdis_dict_equvi_ETG")
#%%
#calculate the stellar mass, base on two different M/L realtion
ML_select_1 = SPlot.MLRelationIband(A["mag_g"],A["mag_i"]).Into13_MassRatio
M_1 = SPlot.MassCalculation(A["sph_mag"], A["Dist"], M_sun,A["mag_g"],A["mag_i"])
mass_1 = M_1.cal_Mass(ML_select_1)
T_M_1 = SPlot.MassCalculation(A["total_mag"], A["Dist"], M_sun,A["mag_g"],A["mag_i"])
T_mass_1 = T_M_1.cal_Mass(ML_select_1)
ML_select_2 = SPlot.MLRelationIband(A["mag_g"],A["mag_i"]).Taylor11_MassRatio
M_2 = SPlot.MassCalculation(A["sph_mag"], A["Dist"], M_sun,A["mag_g"],A["mag_i"])
mass_2 = M_2.cal_Mass(ML_select_2)
T_M_2 = SPlot.MassCalculation(A["total_mag"], A["Dist"], M_sun,A["mag_g"],A["mag_i"])
T_mass_2 = T_M_2.cal_Mass(ML_select_2)
##
mag2 = SRead.grab_mag("Gal_bundle_equvi_bin2_cpt", ["Bulge","CoreBulge"])
mag2_total = SRead.grab_parameter("Gal_bundle_equvi_bin2_cpt", ["Total_mag"],0)
Re_kpc2 = R_e_2 * scale2
Mag2 = mag2 - 25 -5*np.log10(DD2) # with new distance system, NED, hyperleda
Lum2 = 10**((Mag2-(4.53))/(-2.5))
#BD for bin2
name2BD = SRead.grab_parameter("Gal_bundle_BD_equvi_bin2_cpt", ["Bulge","CoreBulge"], 1)
R_e_2_BD = SRead.grab_parameter("Gal_bundle_BD_equvi_bin2_cpt", ["Bulge","CoreBulge"], 1) #get Re
mag2_BD = SRead.grab_mag("Gal_bundle_BD_equvi_bin2_cpt", ["Bulge","CoreBulge"])
Re_kpc2_BD = R_e_2_BD * scale2
Mag2_BD = mag2_BD - 25 -5*np.log10(DD2)
Lum2_BD = 10**((Mag2_BD-(4.53))/(-2.5))
ML_select2 = SPlot.MLRelationIband(AbsMag_g_bin2,AbsMag_i_bin2).Into13_MassRatio
M2 = SPlot.MassCalculation(mag2, DD2, M_sun,AbsMag_g_bin2,AbsMag_i_bin2)
M2_total_old = SPlot.MassCalculation(mag2_total, dc2, M_sun,AbsMag_g_bin2,AbsMag_i_bin2)
E2 = M2.cal_Mass(ML_select2)
E2_total_old = M2_total_old.cal_Mass(ML_select2)
M2_BD = SPlot.MassCalculation(mag2_BD, DD2, M_sun,AbsMag_g_bin2,AbsMag_i_bin2)
E2_BD = M2_BD.cal_Mass(ML_select2)
Re_2_kpc)
Re_3_kpc_lerr, Re_3_kpc_uerr = abs(Re_3 * scale3_lerr -
Re_3_kpc), abs(Re_3 * scale3_uerr -
Re_3_kpc)
Re_1_kpc_err = [Re_1_kpc_lerr, Re_1_kpc_uerr]
Re_2_kpc_err = [Re_2_kpc_lerr, Re_2_kpc_uerr]
Re_3_kpc_err = [Re_3_kpc_lerr, Re_3_kpc_uerr]
Sersic2D_50rad_1_kpc = Sersic2D_50rad_1 * scale1
Sersic2D_50rad_2_kpc = Sersic2D_50rad_2 * scale2
Sersic2D_50rad_3_kpc = Sersic2D_50rad_3 * scale3
############# calculating spheroid mass ########
ML_select1_IP13 = SPlot.MLRelationIband(mag_g1, mag_i1).Into13_MassRatio
ML_select1_R15BC = SPlot.MLRelationIband(mag_g1,
mag_i1).Roediger15BC03_MassRatio
ML_select1_Z09 = SPlot.MLRelationIband(mag_g1, mag_i1).Zibetti09_MassRatio
ML_select1_T11 = SPlot.MLRelationIband(mag_g1, mag_i1).Taylor11_MassRatio
M1 = SPlot.MassCalculation(sph_mag1, D1, 4.53, mag_g1, mag_i1)
E1_IP13 = M1.cal_Mass(ML_select1_IP13)
E1_R15BC = M1.cal_Mass(ML_select1_R15BC)
E1_Z09 = M1.cal_Mass(ML_select1_Z09)
E1_T11 = M1.cal_Mass(ML_select1_T11)
ML_select2_IP13 = SPlot.MLRelationIband(mag_g2, mag_i2).Into13_MassRatio
ML_select2_R15BC = SPlot.MLRelationIband(mag_g2,
mag_i2).Roediger15BC03_MassRatio
def plot_sizemass_trans_3plots(xo_0=None,
yo_0=None,
xn_0=None,
yn_0=None,
name0=None,
xo_1=None,
yo_1=None,
xn_1=None,
yn_1=None,
name1=None,
xo_2=None,
yo_2=None,
xn_2=None,
yn_2=None,
name2=None,
xo_3=None,
yo_3=None,
xn_3=None,
yn_3=None,
name3=None,
xo_4=None,
yo_4=None,
xn_4=None,
yn_4=None,
name4=None,
xo_5=None,
yo_5=None,
xn_5=None,
yn_5=None,
name5=None,
xo_6=None,
yo_6=None,
xn_6=None,
yn_6=None,
name6=None,
xo_7=None,
yo_7=None,
xn_7=None,
yn_7=None,
name7=None,
xo_8=None,
yo_8=None,
xn_8=None,
yn_8=None,
name8=None):
fig = plt.figure()
gs = gridspec.GridSpec(ncols=3, nrows=3, hspace=0, wspace=0.0)
axt0 = plt.subplot(gs[0])
plot_sizemass_trans(axt0, xo_0, yo_0, xn_0, yn_0)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
"",
alpha0=0,
AX=axt0)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
"",
alpha0=0,
AX=axt0)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
"",
alpha0=0,
AX=axt0)
#add_arrow(axt0,xo_0,yo_0,xn_0,yn_0)
#SPlot.ShowcaseIndi.show_name(xo_0,yo_0, name0, A=axt0,size=16)
axt0.text(text_location[0],
text_location[1],
r"$\rm Bin 1$",
fontsize=22,
color="#a5200b")
axt0.text(text_location[0],
text_location[1] - delta_text,
r"$\rm E -> E$",
fontsize=22,
color="k")
axt0.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt0.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt0.set_xscale('log')
axt0.set_yscale('log')
axt0.set_ylabel(r"$\rm R_{e}$ (kpc)", fontsize=16)
axt0.set_xlabel(r"$\rm M_{*} / M_{\odot}$", fontsize=16)
#axt0.legend(loc=4)
#axt0.grid(True)
axt1 = plt.subplot(gs[1], sharey=axt0)
plot_sizemass_trans(axt1, xo_1, yo_1, xn_1, yn_1)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt1)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt1)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt1)
#add_arrow(axt1,xo_1,yo_1,xn_1,yn_1)
#SPlot.ShowcaseIndi.show_name(xo_1,yo_1, name1, A=axt1,size=16)
axt1.text(text_location[0],
text_location[1],
r"$\rm Bin 1$",
fontsize=22,
color="#a5200b")
axt1.text(text_location[0],
text_location[1] - delta_text,
r"$\rm S0 -> E$",
fontsize=22,
color="k")
axt1.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt1.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt1.set_xscale('log')
axt1.set_yscale('log')
axt1.set_xlabel(r"$\rm M_{*} / M_{\odot}$", fontsize=16)
#axt1.legend(loc=4)
#axt1.grid(True)
plt.setp(axt1.get_yticklabels(), visible=False)
axt2 = plt.subplot(gs[2], sharey=axt0)
plot_sizemass_trans(axt2, xo_2, yo_2, xn_2, yn_2)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt2)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt2)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt2)
#add_arrow(axt2,xo_2,yo_2,xn_2,yn_2)
#SPlot.ShowcaseIndi.show_name(xo_2,yo_2, name2, A=axt2,size=16)
axt2.text(text_location[0],
text_location[1],
r"$\rm Bin 1$",
fontsize=22,
color="#a5200b")
axt2.text(text_location[0],
text_location[1] - delta_text,
r"$\rm S -> E$",
fontsize=22,
color="k")
axt2.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt2.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt2.set_xscale('log')
axt2.set_yscale('log')
axt2.set_xlabel(r"$\rm M_{*} / M_{\odot}$", fontsize=16)
#axt2.legend(loc=4)
#axt2.grid(True)
plt.setp(axt2.get_yticklabels(), visible=False)
axt3 = plt.subplot(gs[3])
plot_sizemass_trans(axt3, xo_3, yo_3, xn_3, yn_3)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt3)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt3)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt3)
#add_arrow(axt3,xo_3,yo_3,xn_3,yn_3)
#SPlot.ShowcaseIndi.show_name(xo_3,yo_3, name3, A=axt3,size=16)
axt3.text(text_location[0],
text_location[1],
r"$\rm Bin 2$",
fontsize=22,
color="#0b5786")
axt3.text(text_location[0],
text_location[1] - delta_text,
r"$\rm E -> E$",
fontsize=22,
color="k")
axt3.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt3.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt3.set_xscale('log')
axt3.set_yscale('log')
axt3.set_ylabel(r"$\rm R_{e}$ (kpc)", fontsize=16)
#axt3.legend(loc=4)
#axt3.grid(True)
plt.setp(axt3.get_xticklabels(), visible=False)
axt4 = plt.subplot(gs[4])
plot_sizemass_trans(axt4, xo_4, yo_4, xn_4, yn_4)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt4)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt4)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt4)
#add_arrow(axt4,xo_4,yo_4,xn_4,yn_4)
#SPlot.ShowcaseIndi.show_name(xo_4,yo_4, name4, A=axt4,size=16)
axt4.text(text_location[0],
text_location[1],
r"$\rm Bin 2$",
fontsize=22,
color="#0b5786")
axt4.text(text_location[0],
text_location[1] - delta_text,
r"$\rm S0 -> E$",
fontsize=22,
color="k")
axt4.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt4.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt4.set_xscale('log')
axt4.set_yscale('log')
#axt4.legend(loc=4)
#axt4.grid(True)
plt.setp(axt4.get_yticklabels(), visible=False)
axt5 = plt.subplot(gs[5])
plot_sizemass_trans(axt5, xo_5, yo_5, xn_5, yn_5)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt5)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt5)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt5)
#add_arrow(axt5,xo_5,yo_5,xn_5,yn_5)
#SPlot.ShowcaseIndi.show_name(xo_5,yo_5, name5, A=axt5,size=16)
axt5.text(text_location[0],
text_location[1],
r"$\rm Bin 2$",
fontsize=22,
color="#0b5786")
axt5.text(text_location[0],
text_location[1] - delta_text,
r"$\rm S -> E$",
fontsize=22,
color="k")
axt5.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt5.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt5.set_xscale('log')
axt5.set_yscale('log')
#axt5.legend(loc=4)
#axt5.grid(True)
plt.setp(axt5.get_yticklabels(), visible=False)
axt6 = plt.subplot(gs[6])
plot_sizemass_trans(axt6, xo_6, yo_6, xn_6, yn_6, label_new="Spheroids")
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
"Barro et al. 2013",
alpha0=0,
AX=axt6)
SPlot.SelectionCut(mass0,
Dist0).plot_cut_specific("vdWel",
"van der Wel et al. 2014",
alpha0=0,
AX=axt6)
SPlot.SelectionCut(mass0,
Dist0).plot_cut_specific("vDokkum",
"van Dokkum et al. 2015",
alpha0=0,
AX=axt6)
#add_arrow(axt6,xo_6,yo_6,xn_6,yn_6)
#SPlot.ShowcaseIndi.show_name(xo_6,yo_6, name6, A=axt6,size=16)
axt6.text(text_location[0],
text_location[1],
r"$\rm Bin 3$",
fontsize=22,
color="#2a3236")
axt6.text(text_location[0],
text_location[1] - delta_text,
r"$\rm E -> E$",
fontsize=22,
color="k")
axt6.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt6.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt6.set_xscale('log')
axt6.set_yscale('log')
axt6.legend(loc=3)
#axt6.grid(True)
axt6.set_ylabel(r"$\rm R_{e}$ (kpc)", fontsize=16)
axt6.set_xlabel(r"$\rm M_{*} / M_{\odot}$", fontsize=16)
axt7 = plt.subplot(gs[7])
plot_sizemass_trans(axt7, xo_7, yo_7, xn_7, yn_7)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt7)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt7)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt7)
#add_morph_marker(axt2,E3_R15BC,Re_3_kpc)
#add_arrow(axt7,xo_7,yo_7,xn_7,yn_7)
#SPlot.ShowcaseIndi.show_name(xo_7,yo_7, name7, A=axt7,size=16)
axt7.text(text_location[0],
text_location[1],
r"$\rm Bin 3$",
fontsize=22,
color="#2a3236")
axt7.text(text_location[0],
text_location[1] - delta_text,
r"$\rm S0 -> E$",
fontsize=22,
color="k")
axt7.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt7.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt7.set_xscale('log')
axt7.set_yscale('log')
axt7.set_xlabel(r"$\rm M_{*} / M_{\odot}$", fontsize=16)
#axt7.legend(loc=4)
#axt7.grid(True)
plt.setp(axt7.get_yticklabels(), visible=False)
axt8 = plt.subplot(gs[8])
plot_sizemass_trans(axt8, xo_8, yo_8, xn_8, yn_8)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("Barro",
None,
alpha0=0,
AX=axt8)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vdWel",
None,
alpha0=0,
AX=axt8)
SPlot.SelectionCut(mass0, Dist0).plot_cut_specific("vDokkum",
None,
alpha0=0,
AX=axt8)
#add_morph_marker(axt2,E3_R15BC,Re_3_kpc)
#add_arrow(axt8,xo_8,yo_8,xn_8,yn_8)
#SPlot.ShowcaseIndi.show_name(xo_8,yo_8, name8, A=axt8,size=18)
axt8.text(text_location[0],
text_location[1],
r"$\rm Bin 3$",
fontsize=22,
color="#2a3236")
axt8.text(text_location[0],
text_location[1] - delta_text,
r"$\rm S -> E$",
fontsize=22,
color="k")
axt8.set_xlim(left=xlim_mo[0], right=xlim_mo[1])
axt8.set_ylim(bottom=ylim_mo[0], top=ylim_mo[1])
axt8.set_xscale('log')
axt8.set_yscale('log')
axt8.set_xlabel(r"$\rm M_{*} / M_{\odot}$", fontsize=16)
#axt8.legend(loc=4)
#axt8.grid(True)
plt.setp(axt8.get_yticklabels(), visible=False)
plt.show()
dtype='str')
K_table2_n = SRead.read_table(
"/home/dexter/result/stat/completeness/diagonal_selection_bag3_Bin2V_Kcorr.dat",
dtype='str')
K_table3_n = SRead.read_table(
"/home/dexter/result/stat/completeness/diagonal_selection_bag3_Bin3V_Kcorr.dat",
dtype='str')
K_name1, K_name2, K_name3 = K_table1_n[:, 4], K_table2_n[:, 4], K_table3_n[:,
4]
mag_g1_kcorr, mag_i1_kcorr = K_table1[:, 19], K_table1[:, 18]
mag_g2_kcorr, mag_i2_kcorr = K_table2[:, 19], K_table2[:, 18]
mag_g3_kcorr, mag_i3_kcorr = K_table3[:, 19], K_table3[:, 18]
ML_select1_IP13_K = SPlot.MLRelationIband(mag_g1_kcorr,
mag_i1_kcorr).Into13_MassRatio
ML_select1_R15BC_K = SPlot.MLRelationIband(
mag_g1_kcorr, mag_i1_kcorr).Roediger15BC03_MassRatio
ML_select1_Z09_K = SPlot.MLRelationIband(mag_g1_kcorr,
mag_i1_kcorr).Zibetti09_MassRatio
ML_select1_T11_K = SPlot.MLRelationIband(mag_g1_kcorr,
mag_i1_kcorr).Taylor11_MassRatio
M1_K = SPlot.MassCalculation(sph_mag1, D1, 4.53, mag_g1_kcorr, mag_i1_kcorr)
E1_IP13_K = M1_K.cal_Mass(ML_select1_IP13_K)
E1_R15BC_K = M1_K.cal_Mass(ML_select1_R15BC_K)
E1_Z09_K = M1_K.cal_Mass(ML_select1_Z09_K)
E1_T11_K = M1_K.cal_Mass(ML_select1_T11_K)
ML_select2_IP13_K = SPlot.MLRelationIband(mag_g2_kcorr,
Re_kpc = R_e * scale
R_e_BD = SRead.grab_parameter("Gal_bundle_BD_equvi_cpt",
["Bulge", "CoreBulge"], 1) #get Re
Re_kpc_BD = R_e_BD * scale
mag = SRead.grab_mag("Gal_bundle_equvi_cpt", ["Bulge", "CoreBulge"])
mag_BD = SRead.grab_mag("Gal_bundle_BD_equvi_cpt", ["Bulge", "CoreBulge"])
#mag2_total = SRead.grab_parameter("Gal_bundle_equvi_bin2_cpt", ["Total_mag"],0)
A = SRead.read_list("Gal_vdis_dict_equvi")
mag_g, mag_i = A["mag_g"], A["mag_i"]
ML_select = SPlot.MLRelationIband(mag_g, mag_i).Into13_MassRatio
M = SPlot.MassCalculation(mag, DD, 4.53, mag_g, mag_i)
E = M.cal_Mass(ML_select)
M_BD = SPlot.MassCalculation(mag_BD, DD, 4.53, mag_g, mag_i)
E_BD = M_BD.cal_Mass(ML_select)
#SPlot.ShowcaseCompare2.plot_scat_arrow(E_BD,Re_kpc_BD,[],'bo',"B+D", E,Re_kpc,[],'ro',"Multi")
#plt.show()
SPlot.ShowcaseCompare2.plot_seperation_generic(Re_kpc_BD,
Re_kpc,
1,
para_name="",
colour1="blue",
colour2="orange",
Re_3_kpc_err = [Re_3_kpc_lerr, Re_3_kpc_uerr]
################################
#Calculate mass with K-correction
K_table3 = SRead.read_table(
"/home/dexter/result/stat/completeness/diagonal_selection_bag3_Bin3V_Kcorr.dat"
)
K_table3_n = SRead.read_table(
"/home/dexter/result/stat/completeness/diagonal_selection_bag3_Bin3V_Kcorr.dat",
dtype='str')
K_name3 = K_table3_n[:, 4]
mag_g3_kcorr, mag_i3_kcorr = K_table3[:, 19], K_table3[:, 18]
ML_select3_IP13_K = SPlot.MLRelationIband(mag_g3_kcorr,
mag_i3_kcorr).Into13_MassRatio
ML_select3_R15BC_K = SPlot.MLRelationIband(
mag_g3_kcorr, mag_i3_kcorr).Roediger15BC03_MassRatio
ML_select3_Z09_K = SPlot.MLRelationIband(mag_g3_kcorr,
mag_i3_kcorr).Zibetti09_MassRatio
ML_select3_T11_K = SPlot.MLRelationIband(mag_g3_kcorr,
mag_i3_kcorr).Taylor11_MassRatio
M3_K = SPlot.MassCalculation(sph_mag3, D3, 4.53, mag_g3_kcorr, mag_i3_kcorr)
E3_IP13_K = M3_K.cal_Mass(ML_select3_IP13_K)
E3_R15BC_K = M3_K.cal_Mass(ML_select3_R15BC_K)
E3_Z09_K = M3_K.cal_Mass(ML_select3_Z09_K)
E3_T11_K = M3_K.cal_Mass(ML_select3_T11_K)
################################