def galtypeConcentrationBySigma(df):
fig, axes = plt.subplots(2, 2, sharex=False, sharey=False, figsize=(8, 8))
for b in np.arange(len(bands)):
b = bands[b].upper()
ystr = 'c_sigma_' + str(bands[b])
ystr_L = 'c_' + str(bands[b])
mask = df.GALTYPE_COURSE == 'LT'
med = pandasFunctions.rollingmedainXY(df[mask], 'sigma_Mstar_R25',
np.arange(-1.5, 2.5, .2))
axes[1, 0].plot(med.sigma_Mstar_R25, med[ystr], color=cs[b], lw=3)
med = pandasFunctions.rollingmedainXY(df[mask], 'LOGMASS',
np.arange(8.5, 12, .5))
axes[0, 0].plot(med.LOGMASS, med[ystr_L], color=cs[b], lw=3)
mask = df.GALTYPE_COURSE == 'ET'
med = pandasFunctions.rollingmedainXY(df[mask], 'sigma_Mstar_R25',
np.arange(-1.5, 2.5, .2))
axes[1, 1].plot(med.sigma_Mstar_R25, med[ystr], color=cs[b], lw=3)
med = pandasFunctions.rollingmedainXY(df[mask], 'LOGMASS',
np.arange(8.5, 12, .5))
axes[0, 1].plot(med.LOGMASS,
med[ystr_L],
label=bands[b].upper(),
color=cs[b],
lw=3)
axes[0, 0].set_xlabel(r'log(M$_*$) [M$_{\odot}$]')
axes[0, 0].set_ylabel(
r'$\nu$L$_{\nu}$(Band)$_{2kpc}$/$\nu$L$_{\nu}$(Band)$_{R25}$')
axes[0, 1].set_xlabel(r'log(M$_*$) [M$_{\odot}$]')
axes[0, 1].set_ylabel('')
axes[1, 0].set_xlabel(r'log($\Sigma$(M$_*$)) [M$_{\odot}$/pc$^{2}$]')
axes[1, 0].set_ylabel(r'$\Sigma$(Band)$_{2kpc}$ /$\Sigma$(Band)$_{R25}$')
axes[1, 1].set_xlabel(r'log($\Sigma$(M$_*$)) [M$_{\odot}$/pc$^{2}$]')
axes[1, 1].set_ylabel('')
axes[1, 0].set_xlim(xmin=-0.5)
axes[1, 1].set_xlim(xmin=0)
axes[1, 0].set_ylim(0, 5)
axes[1, 1].set_ylim(0, 11)
axes[0, 0].set_title('Late Type', fontsize=16)
axes[0, 1].set_title('Early Type', fontsize=16)
fig.legend(loc="center right", borderaxespad=0.1)
plt.subplots_adjust(right=0.85)
plt.savefig(
'/Users/kessler.363/Thesis/Concentrationz0mgs/plots/concentration_sigma_galtype.png',
bbox_inches='tight')
def galTypeLumSigmaSolLum(df):
fig, axes = plt.subplots(2, 2, sharex=False, sharey=False, figsize=(8, 8))
for b in np.arange(len(bands)):
ystr = 'sigma_' + str(bands[b].upper()) + '_Lsun_1p0R25'
ystr_L = bands[b].upper() + '_Lsun_1p0R25'
dfn = df.copy()
dfn[ystr] = np.log10(dfn[ystr])
dfn[ystr_L] = np.log10(dfn[ystr_L])
mask = df.GALTYPE_COURSE == 'LT'
med = pandasFunctions.rollingmedainXY(dfn.copy()[mask],
'sigma_Mstar_R25',
np.arange(-1.5, 2.5, .2))
axes[1, 0].plot(med.sigma_Mstar_R25, med[ystr], color=cs[b], lw=3)
med = pandasFunctions.rollingmedainXY(dfn.copy()[mask], 'LOGMASS',
np.arange(8.5, 12, .2))
axes[0, 0].plot(med.LOGMASS, med[ystr_L], color=cs[b], lw=3)
mask = df.GALTYPE_COURSE == 'ET'
med = pandasFunctions.rollingmedainXY(dfn.copy()[mask],
'sigma_Mstar_R25',
np.arange(-1.5, 2.5, .2))
axes[1, 1].plot(med.sigma_Mstar_R25, med[ystr], color=cs[b], lw=3)
med = pandasFunctions.rollingmedainXY(dfn.copy()[mask], 'LOGMASS',
np.arange(8.5, 12, .2))
axes[0, 1].plot(med.LOGMASS,
med[ystr_L],
label=bands[b].upper(),
color=cs[b],
lw=3)
axes[0, 0].set_xlabel(r'log(M$_*$) [M$_{\odot}$]')
axes[0, 0].set_ylabel(r'log($\nu$L$_{\nu}$(Band))$_{R25}$ [L$_{\odot}$]')
axes[0, 1].set_xlabel(r'log(M$_*$) [M$_{\odot}$]')
axes[0, 1].set_ylabel('')
axes[1, 0].set_xlabel(r'log($\Sigma$(M$_*$)) [M$_{\odot}$/pc$^{2}$]')
axes[1,
0].set_ylabel(r'log($\Sigma$(Band)$_{R25}$) [L$_{\odot}$ kpc$^{-2}$]')
axes[1, 1].set_xlabel(r'log($\Sigma$(M$_*$)) [M$_{\odot}$/pc$^{2}$]')
axes[1, 1].set_ylabel('')
axes[0, 0].set_title('Late Type', fontsize=16)
axes[0, 1].set_title('Early Type', fontsize=16)
fig.legend(loc="center right", borderaxespad=0.1)
plt.subplots_adjust(right=0.85)
plt.savefig(
'/Users/kessler.363/Thesis/Concentrationz0mgs/plots/R25_Lsun_sigma_galtype.png',
bbox_inches='tight')
def galtypeConcentrationByMassSolLum(df):
fig, axes = plt.subplots(2, 2, sharex=False, sharey=False, figsize=(8, 8))
for b in np.arange(len(bands)):
ystr = 'c_sigma_' + str(bands[b].upper()) + '_Lsun'
ystr_L = 'c_' + bands[b].upper() + '_Lsun'
dfn = df.copy()
dfn[ystr] = np.log10(dfn[ystr])
dfn[ystr_L] = np.log10(dfn[ystr_L])
mask = df.GALTYPE_COURSE == 'LT'
med = pandasFunctions.rollingmedainXY(dfn.copy()[mask], 'LOGMASS',
np.arange(8.5, 12, .2))
axes[1, 0].plot(med.LOGMASS, med[ystr], color=cs[b], lw=3)
axes[0, 0].plot(med.LOGMASS, med[ystr_L], color=cs[b], lw=3)
mask = df.GALTYPE_COURSE == 'ET'
med = pandasFunctions.rollingmedainXY(dfn.copy()[mask], 'LOGMASS',
np.arange(8.5, 12, .2))
axes[1, 1].plot(med.LOGMASS, med[ystr], color=cs[b], lw=3)
axes[0, 1].plot(med.LOGMASS,
med[ystr_L],
label=bands[b].upper(),
color=cs[b],
lw=3)
axes[0, 0].set_ylabel(
r'log($\nu$L$_{\nu}$(Band)$_{2kpc}$/$\nu$L$_{\nu}$(Band)$_{R25}$)')
axes[0, 1].set_ylabel('')
axes[1,
0].set_ylabel(r'log($\Sigma$(Band)$_{2kpc}$ /$\Sigma$(Band)$_{R25}$)')
axes[1, 1].set_ylabel('')
fig.text(0.5, 0.04, r'log(M$_*$) [M$_{\odot}$]', ha='center', fontsize=14)
axes[0, 0].set_title('Late Type', fontsize=16)
axes[0, 1].set_title('Early Type', fontsize=16)
fig.legend(loc="center right", borderaxespad=0.1)
plt.subplots_adjust(right=0.85)
plt.savefig(
'/Users/kessler.363/Thesis/Concentrationz0mgs/plots/concentration_sigma_byLMASS_Lsun_galtype.png',
bbox_inches='tight')
def sixPanelConcentrationMed(df, cols):
test = df[cols].copy()
ltmelt = test.melt(id_vars=['LOGMASS', 'T'])
med = pandasFunctions.rollingmedainXY(test, 'T', np.arange(-5, 10.5, .5))
g = sns.FacetGrid(ltmelt,
col='variable',
col_wrap=2,
height=2.75,
aspect=1,
sharex=True,
sharey=True,
hue='variable',
palette=cs,
despine=False)
g.map_dataframe(sns.scatterplot, x='T', y='value', alpha=0.6, marker='.')
axes = g.fig.axes
for b in np.arange(len(bands)):
axes[b].plot(med['T'], med[bands[b]], color=cs[b], lw=3)
g.fig.subplots_adjust(wspace=0.05, hspace=0.05)
g.add_legend()
g._legend.set_title('')
g.set_titles('')
for lh in g._legend.legendHandles:
lh.set_alpha(1)
lh._sizes = [50]
g.fig.text(x=0.5,
y=0.05,
horizontalalignment='center',
s=r'T',
fontsize=14)
g.fig.text(x=0.05,
y=0.5,
verticalalignment='center',
s=r'log($\nu$L$_{\nu}$ (2kpc) / $\nu$L$_{\nu}$ (R$_e$))',
rotation=90,
fontsize=14)
plt.ylim(-3, 2)
g.fig.subplots_adjust(bottom=.1)
g.fig.subplots_adjust(left=.15)
plt.subplots_adjust(top=0.95)
plt.savefig(
'/Users/kessler.363/Thesis/Concentrationz0mgs/plots/Concentration_T_scatter.png',
bbox_inches='tight')
plt.close('all')
def makeConcentrationReffLineplot(df):
cols = ['w1', 'w2', 'w3', 'w4', 'nuv', 'fuv', 'LOGMASS', 'GALTYPE_COURSE']
test = df[cols].copy()
et = test[test.GALTYPE_COURSE == 'ET']
lt = test[test.GALTYPE_COURSE == 'LT']
medlt = pandasFunctions.rollingmedainXY(lt.copy(), 'LOGMASS',
np.arange(8.5, 11.1, .2))
medet = pandasFunctions.rollingmedainXY(et.copy(), 'LOGMASS',
np.arange(8.5, 11.1, .2))
fig, axes = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(8, 4))
axes = axes.ravel()
ys = [0.5, 0.4, 0.3, 0.2, 0.1, 0]
for b in range(len(bands)):
axes[0].plot(medlt['LOGMASS'],
medlt[bands[b]],
color=cs[b],
lw=3,
alpha=0.8,
label=bandsu[b])
axes[0].annotate('n=' +
str(len(lt[bands[b]][~np.isnan(lt[bands[b]])])),
(10, ys[b]),
fontsize=12,
color=cs[b])
axes[1].annotate('n=' +
str(len(et[bands[b]][~np.isnan(et[bands[b]])])),
(10, ys[b]),
fontsize=12,
color=cs[b])
axes[1].plot(medet['LOGMASS'],
medet[bands[b]],
color=cs[b],
lw=3,
alpha=0.8)
for ax in axes:
ax.set_xlabel('')
ax.set_ylabel('')
axes[0].set_title('Late Type')
axes[1].set_title('Early Type')
fig.text(x=0.05,
y=0.5,
verticalalignment='center',
s=r'log($\nu$L$_{\nu}$ (2kpc) / $\nu$L$_{\nu}$ (R$_e$))',
rotation=90,
fontsize=14)
fig.text(x=0.5,
y=0.05,
horizontalalignment='center',
s=r'log(M$_*$) [M$_{\odot}$]',
fontsize=14)
fig.subplots_adjust(left=.135)
fig.subplots_adjust(bottom=.18)
fig.subplots_adjust(wspace=0.02, hspace=0.02)
fig.legend(loc="center right", borderaxespad=0.9)
plt.subplots_adjust(right=0.85)
plt.savefig(
'/Users/kessler.363/Thesis/Concentrationz0mgs/plots/Concentration_lineplot_Re_galtype.png',
bbox_inches='tight',
metadata={'Code': 'plotFunctions.makeConcentrationReffLineplot'})
plt.close('all')
def sixPanelSFMS(df, galtype, cols):
bandsu = [b.upper() for b in bands]
test = df[cols].copy()
c = dict(zip(test.columns[:6], bandsu))
test = test.rename(columns=c)
lt = test[test.GALTYPE_COURSE == galtype]
lt = lt.drop(columns='GALTYPE_COURSE')
for b in bands:
lt[b.upper()] = np.log10(lt[b.upper()])
ltmelt = lt.melt(id_vars='LOGMASS')
med = pandasFunctions.rollingmedainXY(lt, 'LOGMASS',
np.arange(8.5, 12, .2))
g = sns.FacetGrid(ltmelt,
col='variable',
col_wrap=2,
height=2.75,
aspect=1,
sharex=True,
sharey=True,
hue='variable',
palette=cs,
despine=False)
g.map_dataframe(sns.scatterplot,
x='LOGMASS',
y='value',
alpha=0.6,
marker='.')
axes = g.fig.axes
for b in np.arange(len(bandsu)):
axes[b].plot(med.LOGMASS, med[bandsu[b]], color=cs[b], lw=3)
axes[b].annotate(
'n=' + str(len(lt[bandsu[b]][~np.isnan(lt[bandsu[b]])])), (10, 38),
fontsize=14)
g.fig.subplots_adjust(wspace=0.05, hspace=0.05)
g.add_legend()
g._legend.set_title('')
g.set_titles('')
for lh in g._legend.legendHandles:
lh.set_alpha(1)
lh._sizes = [50]
g.fig.text(x=0.5,
y=0.05,
horizontalalignment='center',
s=r'log(M$_*$) [M$_{\odot}$]',
fontsize=14)
g.fig.text(x=0.05,
y=0.5,
verticalalignment='center',
s=r'log($\nu$L$_{\nu}$(Band)$_{R25}$) [erg/s]',
rotation=90,
fontsize=14)
g.fig.subplots_adjust(bottom=.1)
g.fig.subplots_adjust(left=.15)
plt.subplots_adjust(top=0.95)
if galtype == 'LT':
g.fig.suptitle('Late Type')
else:
g.fig.suptitle('Early Type')
plt.savefig(
'/Users/kessler.363/Thesis/Concentrationz0mgs/plots/SFMS_scatter_' +
galtype + '.png',
bbox_inches='tight')
plt.close('all')
