def ngc3100_NI_Hb():
galaxy = 'ngc3100'
opt='pop'
instrument='vimos'
from plot_results import add_
from errors2 import get_dataCubeDirectory
Prefig(size=(8,8))
fig, ax = plt.subplots()
f = fits.open(get_dataCubeDirectory(galaxy))
header = f[0].header
f.close()
D = Data(galaxy, instrument=instrument, opt=opt)
ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar,
D.components['[NI]d'].flux/D.components['Hbeta'].flux, header,
nodots=True, flux_unbinned=D.unbinned_flux, colorbar=True, ax=ax,
label=r'$\mathrm{\frac{[NI]\lambda\lambda5197,5200}{H\beta}}$',
label_size=1.4)
for o, color in {'radio':'g','CO':'w'}.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, ax, galaxy, nolegend=True, scale=scale)
ax.ax_dis.tick_params(top=True, bottom=True, left=True, right=True,
direction='in', which='major', length=20, width=3, labelsize='large')
ax.ax_dis.tick_params(top=True, bottom=True, left=True, right=True,
direction='in', which='minor', length=10, width=3)
fig.savefig('%s/Documents/paper/vimos/ngc3100_NI_Hb.png' % (
cc.home_dir), dpi=300, bbox_inches='tight')
def H_profile(instrument='vimos'):
from matplotlib import ticker
if instrument=='vimos':
galaxies = np.array(['ic1459', 'ngc0612', 'ngc3100'])
str_galaxies = np.array(['IC 1459', 'NGC 612', 'NGC 3100'])
cols = (4, 5)
line = 'Hbeta'
res = 0.67 # arcsec/pix
elif instrument=='muse':
galaxies = np.array(['ic1459', 'ngc1316'])
str_galaxies = np.array(['IC 1459', 'NGC 1316'])
cols = (1, 2)
line = 'Halpha'
res = 0.2 # arcsec/pix
Prefig(size=np.array((len(galaxies), 0.43*len(galaxies)))*7)
# if instrument=='vimos':
# plt.rc('axes', labelsize='x-large') # facecolor='w'
# plt.rc('xtick.major', size=10, width=2)
# plt.rc('xtick.minor', size=10, width=2)
# plt.rc('ytick.major', size=10, width=2)
# plt.rc('ytick.minor', size=10, width=2)
plt.rc('font', size=10*len(galaxies))
fig, ax = plt.subplots(1, len(galaxies), sharey=True)
analysis_dir = "%s/Data/%s/analysis" % (cc.base_dir, instrument)
galaxiesFile = "%s/galaxies.txt" % (analysis_dir)
x_cent_gals, y_cent_gals = np.loadtxt(galaxiesFile, unpack=True,
skiprows=1, usecols=cols, dtype=int)
galaxy_gals = np.loadtxt(galaxiesFile, skiprows=1, usecols=(0,),
dtype=str)
for i, galaxy in enumerate(galaxies):
print 'H profile:', galaxy
if galaxy == 'ngc1316':
D = Data(galaxy, instrument=instrument, opt='pop_test', overide=True)
else:
D = Data(galaxy, instrument=instrument, opt='pop')
if instrument=='vimos':
if galaxy=='ic1459':
seeing_sigma = np.mean([0.82,0.84,1.20,1.26,1.10,1.27]) * 2.5
norm_r = 2.4
norm_y = 0.1
elif galaxy=='ngc0612':
seeing_sigma = np.mean([0.72,0.78,1.45,1.46,1.08,1.11]) * 2.5
norm_r = 5
norm_y = 0.2
elif galaxy=='ngc3100':
seeing_sigma = np.mean([0.76,0.82,0.75,0.82,1.16,1.20]) * 2.5
norm_r = 2.9
norm_y = 0.2
elif instrument=='muse':
from errors2_muse import get_dataCubeDirectory
f=fits.open(get_dataCubeDirectory(galaxy))
seeing_sigma = np.mean([f[0].header['ESO TEL AMBI FWHM START'],
f[0].header['ESO TEL AMBI FWHM START']]) * 2.5
if galaxy=='ic1459':
norm_r = 1.7
norm_y = 0.1
elif galaxy=='ngc1316':
norm_r = 3
norm_y = 0.1
i_gal = np.where(galaxy_gals==galaxy)[0][0]
center = (x_cent_gals[i_gal], y_cent_gals[i_gal])
r = np.sqrt((D.xBar - center[0])**2 + (D.yBar - center[1])**2) * res
if line in D.e_components:
H = D.e_line[line].flux
ax[i].errorbar(r, H/np.nanmax(H), yerr=H.uncert/np.nanmax(H),
fmt='.', color='k')
o = np.argsort(r)
lim = ax[i].get_xlim()
ax[i].set_xlim(0, min(lim[1], 22))
lim = ax[i].get_xlim()
x = np.arange(-seeing_sigma, lim[1], 0.01)
y = 1/x**2
y = ndimage.gaussian_filter1d(y, seeing_sigma) # convolve with seeing
y = norm_y * norm_r**2 * y # normalised by eye
ax[i].plot(x[x>=0], y[x>=0], zorder=10, color='r')
ax[i].set_xlim(lim)
ax[i].text(0.93*lim[1], 0.7, str_galaxies[i], ha='right')
ax[i].set_ylim([0.01, 1.1])
ax[i].set_yscale('log')
ax[i].tick_params(which='major', direction='in', length=10,
width=2)
ax[i].tick_params(axis='y', which='minor', direction='in',
length=6, width=2)
for axis in [ax[i].xaxis, ax[i].yaxis]:
axis.set_major_formatter(
ticker.FuncFormatter(lambda y, _: '{:g}'.format(y)))
if instrument == 'vimos':
ax[0].set_ylabel(r'H$\,\beta$ normalised flux')
if instrument == 'muse':
ax[0].set_ylabel(r'H$\,\alpha$ normalised flux')
for a in ax:
a.set_xlabel('Radius (arcsec)')
fig.subplots_adjust(wspace=0,hspace=0)
fig.savefig('%s/Documents/paper/%s_profile.png' % (
cc.home_dir, line), dpi=240, bbox_inches='tight')
plt.close('all')
def ic4296_WHaN2():
print 'WHaN2 for IC 4296'
Prefig()
fig, ax = plt.subplots()
galaxy = 'ic4296'
# galaxies = ['ic1459']
# D = Ds()
analysis_dir = "%s/Data/muse/analysis" % (cc.base_dir)
galaxiesFile = "%s/galaxies.txt" % (analysis_dir)
x_cent_gals, y_cent_gals = np.loadtxt(galaxiesFile, unpack=True,
skiprows=1, usecols=(1,2), dtype=int)
galaxy_gals = np.loadtxt(galaxiesFile, skiprows=1, usecols=(0,),
dtype=str)
i_gal = np.where(galaxy_gals==galaxy)[0][0]
center = (x_cent_gals[i_gal], y_cent_gals[i_gal])
# pickleFile = open('%s/Data/muse/analysis/%s' % (cc.base_dir, galaxy)
# + '/pop/pickled/dataObj.pkl')
# D = pickle.load(pickleFile)
# pickleFile.close()
D = Data(galaxy, instrument='muse', opt='pop')
D.sauron = True
if all([l in D.e_components for l in ['[NII]6583d', 'Halpha']]):
x = np.log10(D.e_line['[NII]6583d'].flux/1.34/D.e_line['Halpha'].flux)
x_err = np.sqrt((D.e_line['[NII]6583d'].flux.uncert/
D.e_line['[NII]6583d'].flux)**2 +
(D.e_line['Halpha'].flux.uncert/D.e_line['Halpha'].flux)**2)/\
np.log(10)
y = np.log10(D.e_line['Halpha'].equiv_width)
y_err = y.uncert
r = np.sqrt((D.xBar-center[0])**2 + (D.yBar-center[1])**2)
m = np.isfinite(x)*np.isfinite(y)*np.isfinite(x_err)*np.isfinite(y_err)*\
np.isfinite(r)
myerrorbar(ax, x[m], y[m], xerr=x_err[m], yerr=y_err[m], color=r[m],
marker='o')
# myerrorbar(ax, x, y, xerr=x_err, yerr=y_err, color=r, marker='o')
ax.set_ylim([-0.1, 1.2])
ax.set_xlim([-1., 1.])
xlim = ax.get_xlim()
ylim = ax.get_ylim()
ax.axhline(np.log10(3), ls=':', c='k')
ax.plot([-0.4, -0.4], [np.log10(3), ylim[1]], ls=':', c='k')
ax.plot([-0.4, xlim[1]], [np.log10(6), np.log10(6)], ls=':', c='k')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_ylabel(r'log(EW(H$_\alpha$)/$\AA$)')
ax.set_xlabel(r'log [NII]$\lambda$6548/H$\,\alpha$')
ax.text(-0.95, 1., 'Star Forming')
ax.text(-0.3, 1., 'Strong AGN')
ax.text(-0.3, 0.65, 'Weak AGN')
ax.text(-0.95, 0.3,'Retired Galaxies')
fig.savefig('%s/Documents/paper/WHaN2.png' % (cc.home_dir), dpi=240)
def WHbN1():
Prefig()
fig, ax = plt.subplots()
galaxies = np.array(['ic1459', 'ic1531', 'ic4296', 'ngc3100', 'ngc3557',
'ngc7075'])
str_galaxies = np.array(['IC 1459', 'IC 1531', 'IC 4296', 'NGC 3100',
'NGC 3557', 'NGC 7075'])
c = np.array(['b', 'orange', 'g', 'r', 'mediumorchid', 'saddlebrown'])
# galaxies = ['ic1459']
# D = Ds()
for i, galaxy in enumerate(galaxies):
print 'WHbN1:', galaxy
D = Data(galaxy, instrument='vimos', opt='pop')
if all([l in D.e_components for l in ['[NI]d', 'Hbeta']]):
x = np.log10(D.e_line['[NI]d'].flux/D.e_line['Hbeta'].flux)
x_err = np.sqrt((D.e_line['[NI]d'].flux.uncert/
D.e_line['[NI]d'].flux)**2 +
(D.e_line['Hbeta'].flux.uncert/D.e_line['Hbeta'].flux)**2)/\
np.log(10)
y = np.log10(D.e_line['Hbeta'].equiv_width)
y_err = y.uncert
m = (D.components['[NI]d'].equiv_width < 0.5) * (
D.components['Hbeta'].equiv_width < EqW_Ha_to_EqW_Hb(0.5))
ax.errorbar(x[~m], y[~m], xerr=x_err[~m], yerr=y_err[~m],
label=str_galaxies[i], fmt='o', ms=9, c=c[i])
ax.errorbar(x[m], y[m], xerr=x_err[m], yerr=y_err[m], fmt='x', ms=9,
c=c[i])
ax.legend(loc=3)
ax.set_ylim([-1., 0.6])
ax.set_xlim([-1.75, 0.75])
xlim = ax.get_xlim()
ylim = ax.get_ylim()
ax.axhline(np.log10(EqW_Ha_to_EqW_Hb(3)), ls=':', c='k')
ax.plot([log_NII_Ha_to_NI_Hb(-0.4), log_NII_Ha_to_NI_Hb(-0.4)],
[np.log10(EqW_Ha_to_EqW_Hb(3)), ylim[1]], ls=':', c='k')
ax.plot([log_NII_Ha_to_NI_Hb(-0.4), xlim[1]],
[np.log10(EqW_Ha_to_EqW_Hb(6)), np.log10(EqW_Ha_to_EqW_Hb(6))], ls=':',
c='k')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_ylabel(r'log(EW(H$_\beta$)/$\AA$)')
ax.set_xlabel(r'log [NI]$\lambda\lambda$5197,5200/H$\,\beta$')
ax.text(-1.65, 0.45, 'Star Forming')
ax.text(-0.95, 0.45, 'Strong AGN')
ax.text(-0.95, 0.1, 'Weak AGN')
ax.text(-1.65, -0.2,'Retired Galaxies')
fig.savefig('%s/Documents/paper/WHbN1.png' % (cc.home_dir), dpi=240)
def SAURON():
Prefig(size=(13,10))
fig, ax = plt.subplots()
galaxies = np.array(['eso443-g024', 'ic1459', 'ic1531', 'ic4296',
'ngc0612', 'ngc1399', 'ngc3100', 'ngc3557', 'ngc7075',
'pks0718-34'])
str_galaxies = np.array(['ESO 443-G24', 'IC 1459', 'IC 1531', 'IC 4296',
'NGC 612', 'NGC 1399', 'NGC 3100', 'NGC 3557', 'NGC 7075',
'PKS 718-34'])
# galaxies = ['ic1459']
# D = Ds()
for i, galaxy in enumerate(galaxies):
# pickleFile = open('%s/Data/vimos/analysis/%s' % (cc.base_dir, galaxy)
# + '/pop/pickled/dataObj.pkl')
# D = pickle.load(pickleFile)
# pickleFile.close()
D = Data(galaxy, instrument='vimos', opt='pop')
if all([l in D.e_components for l in ['[NI]d', 'Hbeta',
'[OIII]5007d']]):
x = np.log10(D.e_line['[NI]d'].flux/D.e_line['Hbeta'].flux)
x_err = np.sqrt((D.e_line['[NI]d'].flux.uncert/
D.e_line['[NI]d'].flux)**2 +
(D.e_line['Hbeta'].flux.uncert/D.e_line['Hbeta'].flux)**2)/\
np.log(10)
y = np.log10(D.e_line['[OIII]5007d'].flux/1.35
/ D.e_line['Hbeta'].flux)
y_err = np.sqrt((D.e_line['[OIII]5007d'].flux.uncert/
D.e_line['[OIII]5007d'].flux)**2 + (
D.e_line['Hbeta'].flux.uncert/D.e_line['Hbeta'].flux)**2) \
/ np.log(10)
ax.errorbar(x, y, xerr=x_err, yerr=y_err, label=str_galaxies[i],
fmt='o', ms=9)
ax.legend()
ax.text(0, 1.2, 'Seyfert 2/LINER')
ax.text(-1.9, -0.2, 'Star-forming')
ax.text(-0.7, -0.75, 'Composite', rotation=280, va='center', ha='center')
ax.set_xlabel(r'$\log\left(\frac{[NI]\lambda\lambda5197,5200}{H\beta}\right)$')
ax.set_ylabel(r'$\log\left(\frac{[OIII]\lambda5007}{H\beta}\right)$')
ax.set_xlim([-2., 1.])
ax.set_ylim([-1.5, 1.5])
xlim = ax.get_xlim()
x_line = np.linspace(xlim[0], xlim[1], 100)
y_line = 0.61/(x_line - 0.47) + 1.19
m = y_line < 1
plt.plot(log_NII_Ha_to_NI_Hb(x_line[m]), y_line[m], 'k')
y_line2 = 0.61/(x_line - 0.05) + 1.3
m1 = y_line2 < y_line
a = np.min(x_line[m1])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 0.61/(x_line2 - 0.05) + 1.3
m2 = y_line2 < 1
ax.plot(log_NII_Ha_to_NI_Hb(x_line2[m2]), y_line2[m2],'k--')
## Add MAPPINGS-III grids
add_grids(ax, '[NI]', '[OIII]')
ax.set_xlim([-2., 1.])
ax.set_ylim([-1.5, 1.5])
fig.savefig('%s/Documents/paper/SAURON.png' % (cc.home_dir), dpi=240,
bbox_inches='tight')
def BPT():
from errors2_muse import get_dataCubeDirectory
opt = 'pop'
instrument = 'muse'
Prefig(size=np.array((3, 2))*6, transparent=False)
fig, ax = plt.subplots(2,3, sharey=True)
analysis_dir = "%s/Data/muse/analysis" % (cc.base_dir)
galaxiesFile = "%s/galaxies.txt" % (analysis_dir)
x_cent_gals, y_cent_gals = np.loadtxt(galaxiesFile, unpack=True,
skiprows=1, usecols=(1,2), dtype=int)
galaxy_gals = np.loadtxt(galaxiesFile, skiprows=1, usecols=(0,),
dtype=str)
for j, galaxy in enumerate(['ic1459','ngc1316']):
i_gal = np.where(galaxy_gals==galaxy)[0][0]
center = (x_cent_gals[i_gal], y_cent_gals[i_gal])
D = Data(galaxy, instrument=instrument, opt=opt)
opt = D.opt
r = np.sqrt((D.xBar - center[0])**2 + (D.yBar - center[1])**2)
for i, l in enumerate(['[NII]6583d', '[SII]6716', '[OI]6300d']):
y = np.log10(D.e_line['[OIII]5007d'].flux/1.35
/ D.e_line['Hbeta'].flux)
x = np.log10(D.e_line[l].flux/D.e_line['Halpha'].flux)
y_err = np.sqrt((D.e_line['[OIII]5007d'].flux.uncert/
D.e_line['[OIII]5007d'].flux)**2 +
(D.e_line['Hbeta'].flux.uncert/D.e_line['Hbeta'].flux)**2)\
/ np.log(10)
x_err = np.sqrt((D.e_line[l].flux.uncert/D.e_line[l].flux)**2 +
(D.e_line['Halpha'].flux.uncert/D.e_line['Halpha'].flux)**2)\
/ np.log(10)
large_err = (x_err > 0.5) + (y_err > 0.5)
Seyfert2_combined = np.ones(len(x)).astype(bool)
LINER_combined = np.ones(len(x)).astype(bool)
SF_combined = np.ones(len(x)).astype(bool)
x_line1 = np.arange(-2.2, 1, 0.001)
if l == '[SII]6716':
ax[j, i].text(-0.7, 1.2, 'Seyfert 2')
ax[j, i].text(-1.1, -0.2, 'Star-forming')
ax[j, i].text(0.2, -0.5, 'LINER')
Seyfert2 = ((0.72/(x - 0.32) + 1.30 < y) + (x > 0.32)) \
* (1.89 * x + 0.76 < y) * ~large_err
LINER = ((0.72/(x - 0.32) + 1.30 < y) + (x > 0.32)) \
* (1.89 * x + 0.76 > y) * ~large_err
SF = (y < 0.72/(x - 0.32) + 1.30) * (x < 0.32) * ~large_err
y_line1 = 0.72/(x_line1 - 0.32) + 1.30
m = y_line1 < 1
ax[j, i].plot(x_line1[m], y_line1[m],'k')
y_line2 = 1.89 * x_line1 + 0.76
m = y_line2 > y_line1
a = np.min(x_line1[m])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 1.89 * x_line2 + 0.76
ax[j, i].plot(x_line2, y_line2, 'k')
lab = r'$\log\left(\frac{[SII]\lambda\lambda6717,6731}{H\alpha}\right)$'
ax[j, i].set_xlim([-1.2, 0.7])
elif l == '[NII]6583d':
x /= 1.34
ax[j, i].text(-0.5, 1.2, 'Seyfert 2/LINER')
ax[j, i].text(-1.9, -0.2, 'Star-forming')
ax[j, i].text(-0.1, -0.75, 'Composite', rotation=280, va='center',
ha='center')
Seyfert2 = ((0.61/(x - 0.47) + 1.19 < y) + (x > 0.47)) \
* ~large_err
LINER = ((0.61/(x - 0.47) + 1.19 < y) + (x > 0.47)) \
* ~large_err
SF = (0.61/(x - 0.47) + 1.19 > y) * (x < 0.47) * ~large_err
y_line1 = 0.61/(x_line1 - 0.47) + 1.19
m = y_line1 < 1
ax[j, i].plot(x_line1[m], y_line1[m],'k')
y_line2 = 0.61/(x_line1 - 0.05) + 1.3
m1 = y_line2 < y_line1
a = np.min(x_line1[m1])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 0.61/(x_line2 - 0.05) + 1.3
m2 = y_line2 < 1
ax[j, i].plot(x_line2[m2], y_line2[m2],'k--')
lab = r'$\log\left(\frac{[NII]\lambda6584}{H\alpha}\right)$'
ax[j, i].set_xlim([-2, 1])
# add_grids(ax[j, i], '[NII]','[OIII]', x_Ha=True)
elif l == '[OI]6300d':
x /= 1.33
ax[j, i].text(-1.5, 1.2, 'Seyfert 2')
ax[j, i].text(-2.0, -0.2, 'Star-forming')
ax[j, i].text(-0.6, -0.5, 'LINER')
Seyfert2 = ((y > 0.73/(x + 0.59) + 1.33) + (x > -0.59)) \
* (y > 1.18 * x + 1.30) * ~large_err
LINER = ((y > 0.73/(x + 0.59) + 1.33) + (x > -0.59)) \
* (y < 1.18 * x + 1.30) * ~large_err
SF = (y < 0.73/(x + 0.59) + 1.33) * (x < -0.59) * ~large_err
y_line1 = 0.73/(x_line1 + 0.59) + 1.33
m = y_line1 < 1
ax[j, i].plot(x_line1[m], y_line1[m],'k')
y_line2 = 1.18 * x_line1 + 1.30
m = y_line2 > y_line1
a = np.min(x_line1[m])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 1.18 * x_line2 + 1.30
ax[j, i].plot(x_line2, y_line2, 'k')
# ax[j, i].axvline(-0.59, ls='--', c='k')
lab = r'$\log\left(\frac{[OI]\lambda6300}{H\alpha}\right)$'
ax[j, i].set_xlim([-2.2, 0])
# add_grids(ax[i], '[OI]', '[OIII]', x_Ha=True)
ax[j, i].set_ylim([-1.2, 1.5])
Seyfert2_combined *= Seyfert2
LINER_combined *= LINER
SF_combined *= SF
myerrorbar(ax[j, i], x, y, xerr=x_err, yerr=y_err, marker='.',
color=r)
# ax[1, i].set_xlabel(r'%sH$\alpha$)' % (lab), size='x-large')
ax[1, i].set_xlabel(lab, size='x-large')
y_loc = np.mean([ax[0,0].get_position().y0, ax[1,0].get_position().y1])
fig.text(ax[0,0].get_position().x0-0.03, y_loc,
r'$\log\left(\frac{[OIII]\lambda5007}{H\beta}\right)$', va='center', ha='right',
rotation='vertical', size='x-large')
for i, g in enumerate(['IC 1459', 'NGC 1316']):
loc = np.mean([ax[i,0].get_position().y0,
ax[i,0].get_position().y1])
print g
fig.text(0.06, loc, g, va='center', ha='right',
rotation='vertical', size='xx-large')
for a in ax[0,:]:
a.set_xticklabels([])
saveTo = '%s/Documents/paper/BPT.png' % (cc.home_dir)
fig.subplots_adjust(wspace=0,hspace=0)
fig.savefig(saveTo, dpi=240)
plt.close()
def ngc1316_inflow():
galaxy = 'ngc1316'
opt = 'pop'
instrument = 'muse'
from plot_results_muse import add_
from errors2_muse import get_dataCubeDirectory
import disk_fit_functions_binned as dfn
Prefig(size=np.array((3.6, 1))*5.5)
fig, ax = plt.subplots(1,3)
f = fits.open(get_dataCubeDirectory(galaxy))
header = f[1].header
f.close()
D = Data('ngc1316', instrument=instrument, opt=opt)
vel = D.components['[OIII]5007d'].plot['vel']
m = ~np.isnan(vel)
d, params = dfn.disk_fit_exp(D.xBar[m], D.yBar[m], np.array(vel).copy()[m],
vel.uncert.copy()[m], sigclip=5.0, leeway=0., verbose=False, grid_length=150)
d *= -1 # Seems to be slight quirk of the system.
params[5:] *= -1
disc = vel.copy() # reinsert nans
disc[m] = d
vmin, vmax = set_lims(D.components['[OIII]5007d'].plot['vel'],
symmetric=True, positive=False)
ax[0] = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar,
vel, header, vmin=vmin, max=vmax, nodots=True, flux_unbinned=D.unbinned_flux,
colorbar=False, ax=ax[0])
ax[1] = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar,
disc, header, vmin=vmin, vmax=vmax, nodots=True,
flux_unbinned=D.unbinned_flux, colorbar=False, ax=ax[1])
ax[2] = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar,
vel - disc, header, vmin=vmin, vmax=vmax, nodots=True,
flux_unbinned=D.unbinned_flux, colorbar=False, ax=ax[2])
for a in ax:
for o, color in {'radio':'g','CO':'w'}.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, a, galaxy, nolegend=True, scale=scale)
if hasattr(a, 'ax_dis'):
a.ax_dis.tick_params(top=True, bottom=True, left=True,
right=True, direction='in', which='major', length=20,
width=3, labelsize='large')
a.ax_dis.tick_params(top=True, bottom=True, left=True,
right=True, direction='in', which='minor', length=10,
width=3)
# Decrease gap between maps
for i in range(len(ax)):
for a in ax[i:]:
ax_loc = a.get_position()
ax_loc.x0 -= 0.03
ax_loc.x1 -= 0.03
a.set_position(ax_loc)
if hasattr(a, 'ax_dis'):
a.ax_dis.set_position(ax_loc)
for i, t in enumerate([r'$V_\mathrm{gas}$', r'$V_\mathrm{model}$',
r'$V_\mathrm{residuals} = V_\mathrm{gas} - V_\mathrm{model}$']):
fig.text(ax[i].ax_dis.get_position().x0+0.02,
ax[i].ax_dis.get_position().y1-0.04, t, va='top', color='w', zorder=15)
for a in ax[1:]:
if hasattr(a, 'ax_dis'):
a.ax_dis.set_yticklabels([])
a.ax_dis.set_ylabel('')
ax_loc = ax[2].ax_dis.get_position()
cax = fig.add_axes([ax_loc.x1+0.03, ax_loc.y0, 0.02, ax_loc.height])
cbar = plt.colorbar(ax[0].cs, cax=cax)
# cbar.ax.set_yticklabels([])
fig.text(ax_loc.x1+0.08, 0.5, r'Mean velocity (km s$^{-1}$)',
rotation=270, verticalalignment='center')
fig.savefig('%s/Documents/paper/ngc1316_inflow.png' % (
cc.home_dir), dpi=300, bbox_inches='tight')
def plot(galaxies, str_galaxies, file_name, instrument, debug=False):
if instrument == 'vimos':
from plot_results import add_
from errors2 import get_dataCubeDirectory
elif instrument == 'muse':
from plot_results_muse import add_
from errors2_muse import get_dataCubeDirectory
opt = 'pop'
overplot={'CO':'w', 'radio':'g'}
Prefig(size=np.array((4, len(galaxies)))*7)
fig, axs = plt.subplots(len(galaxies), 4)#, sharex=True, sharey=True)
out_dir = '%s/Documents/paper/%s' % (cc.home_dir, instrument)
for i, galaxy in enumerate(galaxies):
if debug:
from produce_plots import Ds
D = Ds()
else:
D = Data(galaxy, instrument=instrument, opt=opt)
opt = D.opt
print galaxy
vin_dir = '%s/Data/%s/analysis' % (cc.base_dir, instrument)
data_file = "%s/galaxies.txt" % (vin_dir)
file_headings = np.loadtxt(data_file, dtype=str)[0]
col = np.where(file_headings=='SN_%s' % (opt))[0][0]
SN_target_gals = np.loadtxt(data_file,
unpack=True, skiprows=1, usecols=(col,))
galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0,),
dtype=str)
i_gal = np.where(galaxy_gals==galaxy)[0][0]
SN_target=SN_target_gals[i_gal]
attr, vmin, vmax = np.loadtxt('%s/lims.txt' % (vin_dir), dtype=str,
usecols=(0,1,2), skiprows=1, unpack=True)
vmin, vmax = vmin.astype(float), vmax.astype(float)
f = fits.open(get_dataCubeDirectory(galaxy))
if instrument == 'vimos':
header = f[0].header
elif instrument == 'muse':
header = f[1].header
f.close()
plots = [
"components['[OIII]5007d'].plot['vel']",
"components['[OIII]5007d'].plot['sigma']",
"components['[OIII]5007d'].plot['vel'].uncert",
"components['[OIII]5007d'].plot['sigma'].uncert"
]
# Velocity
if galaxy == 'ngc3100':
vmin_vel, vmax_vel = -100, 100 #set_lims(
# D.components['[OIII]5007d'].plot['vel'], symmetric=True,
# n_std=5)
print 'NGC 3100 velocity scale:', vmin_vel,'km/s to ', \
vmax_vel, 'km/s'
else:
vmin_vel=vmin[attr==plots[0]][0]
vmax_vel=vmax[attr==plots[0]][0]
axs[i,0] = plot_velfield_nointerp(D.x, D.y, D.bin_num,
D.xBar, D.yBar, D.components['[OIII]5007d'].plot['vel'], header,
vmin=vmin_vel, vmax=vmax_vel, cmap=sauron,
flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio,
signal_noise_target=SN_target, ax=axs[i,0])
if overplot:
for o, color in overplot.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, axs[i, 0], galaxy, nolegend=True,
scale=scale)
axs[i,1] = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar,
D.yBar, D.components['[OIII]5007d'].plot['vel'].uncert, header,
vmin=vmin[attr==plots[2]][0], vmax=vmax[attr==plots[2]][0],
cmap=sauron, flux_unbinned=D.unbinned_flux,
signal_noise=D.SNRatio, signal_noise_target=SN_target,
ax=axs[i,1])
# Velocty dispersion
axs[i,2] = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar,
D.yBar, D.components['[OIII]5007d'].plot['sigma'], header,
vmin=vmin[attr==plots[1]][0], vmax=vmax[attr==plots[1]][0],
cmap=sauron, flux_unbinned=D.unbinned_flux,
signal_noise=D.SNRatio, signal_noise_target=SN_target,
ax=axs[i,2])
if overplot:
for o, color in overplot.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, axs[i, 2], galaxy, nolegend=True,
scale=scale)
axs[i,3] = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar,
D.yBar, D.components['[OIII]5007d'].plot['sigma'].uncert, header,
vmin=vmin[attr==plots[3]][0], vmax=vmax[attr==plots[3]][0],
cmap=sauron, flux_unbinned=D.unbinned_flux,
signal_noise=D.SNRatio, signal_noise_target=SN_target,
ax=axs[i,3])
for a in axs.flatten():
if hasattr(a, 'ax_dis'):
a.ax_dis.tick_params(top=True, bottom=True, left=True,
right=True, direction='in', which='major', length=20,
width=3, labelsize='large')
a.ax_dis.tick_params(top=True, bottom=True, left=True,
right=True, direction='in', which='minor', length=10,
width=3)
a.ax_dis.xaxis.label.set_size(22)
a.ax_dis.yaxis.label.set_size(22)
for a in axs[:,1:].flatten():
if hasattr(a, 'ax_dis'):
a.ax_dis.set_yticklabels([])
a.ax_dis.set_ylabel('')
# for a in axs[range(0, len(galaxies), 2), 1:].flatten():
# if hasattr(a, 'ax_dis'):
# a.ax_dis.set_yticklabels([])
# a.ax_dis.set_ylabel('')
for a in axs[:-1,:].flatten():
if hasattr(a, 'ax_dis'):
a.ax_dis.set_xticklabels([])
a.ax_dis.set_xlabel('')
# Create gap between galaxies
for i in range(1, len(galaxies)):
for a in axs[i, :].flatten():
ax_loc = a.get_position()
ax_loc.y0 -= i*0.01
ax_loc.y1 -= i*0.01
a.set_position(ax_loc)
if hasattr(a, 'ax_dis'):
a.ax_dis.set_position(ax_loc)
loc = np.mean([axs[0,0].get_position().x0, axs[0,1].get_position().x1])
fig.text(loc, 0.92, r'Mean Velocity', va='top', ha='center', size='xx-large')
loc = np.mean([axs[0,2].get_position().x0, axs[0,3].get_position().x1])
fig.text(loc, 0.92, r'Velocity Dispersion', va='top', ha='center',
size='xx-large')
for i, g in enumerate(str_galaxies):
loc = np.mean([axs[i,0].get_position().y0,
axs[i,0].get_position().y1])
fig.text(0.07, loc, g, va='center', ha='right',
rotation='vertical', size='xx-large')
# Add colorbar
ticks_sym = ticker.MaxNLocator(nbins=4, symmetric=True,
min_n_ticks=6)
ticks_pos = ticker.MaxNLocator(nbins=4, min_n_ticks=3)
ax_loc = axs[0,3].get_position()
# Left
cax = fig.add_axes([ax_loc.x1+0.06, ax_loc.y0, 0.02, ax_loc.height])
cbar = plt.colorbar(axs[0,0].cs, cax=cax, ticks=ticks_pos)
fig.text(ax_loc.x1+0.02, (ax_loc.y0+ax_loc.y1)/2,
r'Velocity (km s$^{-1}$)', rotation=90, va='center', ha='center')
# Right
fig.text(ax_loc.x1+0.13, (ax_loc.y0+ax_loc.y1)/2,
r'Velocity Dispersion (km s$^{-1}$)', rotation=270, va='center',
ha='center')
cax2 = cax.twinx()
cax2.set_ylim(axs[0,2].cs.get_clim())
cax2.yaxis.set_major_locator(ticks_sym)
# Colorbar for Uncertainy
ax_loc = axs[1,3].get_position()
ticks_pos = ticker.MaxNLocator(nbins=4)
# Left
cax = fig.add_axes([ax_loc.x1+0.06, ax_loc.y0, 0.02, ax_loc.height])
cbar = plt.colorbar(axs[1,1].cs, cax=cax, ticks=ticks_pos)
fig.text(ax_loc.x1+0.02, (ax_loc.y0+ax_loc.y1)/2,
r'Velocity Uncertainy (km s$^{-1}$)', rotation=90, va='center',
ha='center')
# Right
fig.text(ax_loc.x1+0.13, (ax_loc.y0+ax_loc.y1)/2,
r'Velocity Dispersion Uncertainy(km s$^{-1}$)', rotation=270,
va='center', ha='center')
cax2 = cax.twinx()
cax2.set_ylim(axs[1,3].cs.get_clim())
cax2.yaxis.set_major_locator(ticks_pos)
# Add extra colorbar for NGC 3100
if 'ngc3100' in galaxies:
loc = np.where(np.array(galaxies)=='ngc3100')[0][0]
ax_loc = axs[loc, 3].get_position()
ticks_sym = ticker.MaxNLocator(nbins=4, min_n_ticks=3)
cax = fig.add_axes([ax_loc.x1+0.06, ax_loc.y0, 0.02, ax_loc.height])
cbar = plt.colorbar(axs[loc,1].cs, cax=cax, ticks=ticks_sym)
fig.text(ax_loc.x1+0.13, (ax_loc.y0+ax_loc.y1)/2,
r'Velocity for NGC 3100 (km s$^{-1}$)', rotation=270,
va='center', ha='center')
if debug:
fig.savefig('%s/%s.png' % (out_dir, 'test'), bbox_inches='tight',
dpi=240)
else:
fig.savefig('%s/%s.png' % (out_dir, file_name), bbox_inches='tight',
dpi=240)
plt.close('all')
def plot(galaxy, instrument='vimos', debug=True):
print galaxy, instrument
# opt = 'kin'
overplot={'CO':'c', 'radio':'g'}
if instrument=='vimos':
from plot_results import add_, set_lims
from errors2 import get_dataCubeDirectory
ext = 0
rows = 5
plots = [[
'D.flux',
"D2.components['[OIII]5007d'].flux",
"D.components['stellar'].plot['vel']",
"D.components['stellar'].plot['vel'].uncert",
"D.components['stellar'].plot['sigma']",
"D.components['stellar'].plot['sigma'].uncert"
],[
"D2.absorption_line('G4300')",
"D2.absorption_line('G4300',uncert=True)[1]",
"D2.absorption_line('Fe4383')",
"D2.absorption_line('Fe4383',uncert=True)[1]",
"D2.absorption_line('Ca4455')",
"D2.absorption_line('Ca4455',uncert=True)[1]"
],[
"D2.absorption_line('Fe4531')",
"D2.absorption_line('Fe4531',uncert=True)[1]",
"D2.absorption_line('H_beta')",
"D2.absorption_line('H_beta',uncert=True)[1]",
"D2.absorption_line('Fe5015')",
"D2.absorption_line('Fe5015',uncert=True)[1]"
],[
"D2.absorption_line('Mg_b')",
"D2.absorption_line('Mg_b',uncert=True)[1]",
'',
'',
'',
''
]]
str_plots = [[
'Flux',
r'[OIII]$\lambda\lambda$4959,5007 Flux',
"Stellar mean velocity",
"Stellar mean velocity\nuncertainty",
"Stellar velocity dispersion",
"Stellar velocity dispersion\nuncertainty"
],[
"G4300",
"G4300 uncertainty",
"Fe4383",
"Fe4383 uncertainty",
"Ca4455",
"Ca4455 uncertainty"
],[
"Fe4531",
"Fe4531 uncertainty",
r'H$\,\beta$',
r'H$\,\beta$ uncertainty',
"Fe5015",
"Fe5015 uncertainty"
],[
r'Mg$\,$b',
r'Mg$\,$b uncertainty',
'',
'',
'',
''
]]
units = [[
'',
'',
r'km s$^{-1}$',
r'km s$^{-1}$',
r'km s$^{-1}$',
r'km s$^{-1}$'
],[
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$'
],[
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$'
],[
r'$\AA$',
r'$\AA$',
'',
'',
'',
''
]]
elif instrument == 'muse':
from plot_results_muse import add_, set_lims
from errors2_muse import get_dataCubeDirectory
ext = 1
rows = 6
plots = [[
'D.flux',
"D2.components['[OIII]5007d'].flux",
"D.components['stellar'].plot['vel']",
"D.components['stellar'].plot['vel'].uncert",
"D.components['stellar'].plot['sigma']",
"D.components['stellar'].plot['sigma'].uncert"
],[
"D2.absorption_line('H_beta')",
"D2.absorption_line('H_beta',uncert=True)[1]",
"D2.absorption_line('Fe5015')",
"D2.absorption_line('Fe5015',uncert=True)[1]",
"D2.absorption_line('Mg_b')",
"D2.absorption_line('Mg_b',uncert=True)[1]"
],[
"D2.absorption_line('Fe5270')",
"D2.absorption_line('Fe5270',uncert=True)[1]",
"D2.absorption_line('Fe5335')",
"D2.absorption_line('Fe5335',uncert=True)[1]",
"D2.absorption_line('Fe5406')",
"D2.absorption_line('Fe5406',uncert=True)[1]"
],[
"D2.absorption_line('Fe5709')",
"D2.absorption_line('Fe5709',uncert=True)[1]",
"D2.absorption_line('Fe5782')",
"D2.absorption_line('Fe5782',uncert=True)[1]",
"D2.absorption_line('NaD')",
"D2.absorption_line('NaD',uncert=True)[1]"
],[
"D2.absorption_line('TiO1',remove_badpix=True)",
"D2.absorption_line('TiO1',uncert=True,remove_badpix=True)[1]",
"D2.absorption_line('TiO2',remove_badpix=True)",
"D2.absorption_line('TiO2',uncert=True,remove_badpix=True)[1]",
'',
''
]]
str_plots = [[
'Flux',
r'[OIII]$\lambda\lambda$4959,5007 Flux',
"Stellar mean velocity",
"Stellar mean velocity\nuncertainty",
"Stellar velocity dispersion",
"Stellar velocity dispersion\nuncertainty"
],[
r'H$\,\beta$',
r'H$\,\beta$ uncertainty',
"Fe5015",
"Fe5015 uncertainty",
r'Mg$\,$b',
r'Mg$\,$b uncertainty'
],[
'Fe5270',
'Fe5270 uncertainty',
'Fe5335',
'Fe5335 uncertainty',
'Fe5406',
'Fe5406 uncertainty'
],[
'Fe5709',
'Fe5709 uncertainty',
'Fe5782',
'Fe5782 uncertainty',
'NaD',
'NaD uncertainty'
],[
'TiO1',
'TiO1 uncertainty',
'TiO2',
'TiO2 uncertainty',
'',
''
]]
units = [[
'',
'',
r'km s$^{-1}$',
r'km s$^{-1}$',
r'km s$^{-1}$',
r'km s$^{-1}$'
],[
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$'
],[
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$'
],[
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$',
r'$\AA$'
],[
'mag',
'mag',
'mag',
'mag',
'',
''
]]
if galaxy in ['ngc0612', 'pks0718-34']:
rows -= 1 # No Mgb
if galaxy in ['ic1459', 'ngc0612', 'ngc1316', 'ngc3100']:
rows += 1
plots.insert(1, [
"D2.components['[OIII]5007d'].plot['vel']",
"D2.components['[OIII]5007d'].plot['vel'].uncert",
"D2.components['[OIII]5007d'].plot['sigma']",
"D2.components['[OIII]5007d'].plot['sigma'].uncert",
'',
''
])
str_plots.insert(1, [
r"[OIII]$\lambda\lambda$4959,5007"+"\nmean velocity",
r"[OIII]$\lambda\lambda$4959,5007"+"\nmean velocity uncertainty",
r"[OIII]$\lambda\lambda$4959,5007"+"\nvelocity dispersion",
r"[OIII]$\lambda\lambda$4959,5007"+"\nvelocity dispersion\nuncertainty",
'',
''
])
units.insert(1,[
r'km s$^{-1}$',
r'km s$^{-1}$',
r'km s$^{-1}$',
r'km s$^{-1}$',
'',
''
])
Prefig(size=np.array((6, rows))*6)
fig, axs = plt.subplots(rows, 6)
out_dir = '%s/Documents/paper/' % (cc.home_dir)
f = fits.open(get_dataCubeDirectory(galaxy))
header = f[ext].header
f.close()
if debug:
D = Ds()
D2 = Ds()
vin_dir = '%s/Data/%s/analysis' % (cc.base_dir, instrument)
data_file = "%s/galaxies.txt" % (vin_dir)
file_headings = np.loadtxt(data_file, dtype=str)[0]
col = np.where(file_headings=='SN_kin')[0][0]
col2 = np.where(file_headings=='SN_pop')[0][0]
SN_target_kin_gals, SN_target_pop_gals = np.loadtxt(data_file,
unpack=True, skiprows=1, usecols=(col,col2))
galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0,),dtype=str)
i_gal = np.where(galaxy_gals==galaxy)[0][0]
SN_target_kin=SN_target_kin_gals[i_gal]
SN_target_pop=SN_target_pop_gals[i_gal]
# attr, vmin, vmax = np.loadtxt('%s/lims.txt' % (vin_dir), dtype=str,
# usecols=(0,1,2), skiprows=1, unpack=True)
# vmin, vmax = vmin.astype(float), vmax.astype(float)
vin_dir2 = str(vin_dir + '/%s/pop' % (galaxy))
vin_dir += '/%s/kin' % (galaxy)
if not debug:
# pickle_file = '%s/pickled' % (vin_dir)
# pickleFile = open("%s/dataObj.pkl" % (pickle_file), 'rb')
# D = pickle.load(pickleFile)
# pickleFile.close()
# pickle_file2 = '%s/pickled' % (vin_dir2)
# pickleFile2 = open("%s/dataObj.pkl" % (pickle_file2), 'rb')
# D2 = pickle.load(pickleFile2)
# pickleFile2.close()
D = Data(galaxy, instrument=instrument, opt='kin')
D2 = Data(galaxy, instrument=instrument, opt='pop')
for i, row in enumerate(plots):
for j, p in enumerate(row):
# print i, j, p
if galaxy in ['ngc0612', 'pks0718-34'] and 'Mg_b' in p:
break # break out of for-loop
elif galaxy in ['ngc1399', 'pks0718-34'] and 'OIII' in p:
axs[i,j].remove()
elif 'flux' in p:
if p == 'D.flux':
axs[i,j] = plot_velfield_nointerp(D.x, D.y, D.bin_num,
D.xBar, D.yBar, eval(p), header, cmap='gist_yarg',
flux_unbinned=D.unbinned_flux, galaxy=str_plots[i][j],
ax=axs[i,j])
elif p == "D2.components['[OIII]5007d'].flux":
axs[i,j] = plot_velfield_nointerp(D2.x, D2.y, D2.bin_num,
D2.xBar, D2.yBar, eval(p), header, cmap='gist_yarg',
flux_unbinned=D2.unbinned_flux, galaxy=str_plots[i][j],
ax=axs[i,j])
if overplot and p != '' and 'uncert' not in p:
for o, color in overplot.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, axs[i,j], galaxy, nolegend=True, scale=scale)
if i > 0:
if plots[i-1][j] != '' and hasattr(axs[i-1,j], 'ax_dis'):
axs[i-1,j].ax_dis.set_xticklabels([])
axs[i-1,j].ax_dis.set_xlabel('')
if j > 0:
if plots[i][j-1] != '' and hasattr(axs[i,j-1], 'ax_dis'):
axs[i,j].ax_dis.set_yticklabels([])
axs[i,j].ax_dis.set_ylabel('')
elif p != '':
vmin, vmax = set_lims(eval(p),
symmetric = 'vel' in p and 'uncert' not in p,
positive = not ('vel' in p and 'uncert' not in p))
if 'D2' in p:
axs[i,j] = plot_velfield_nointerp(D2.x, D2.y, D2.bin_num,
D2.xBar, D2.yBar, eval(p), header, vmin=vmin, vmax=vmax,
flux_unbinned=D2.unbinned_flux, galaxy=str_plots[i][j],
signal_noise=D2.SNRatio, signal_noise_target=SN_target_pop,
ax=axs[i,j], lim_labels=True,
lim_labels_units=units[i][j])
else:
axs[i,j] = plot_velfield_nointerp(D.x, D.y, D.bin_num,
D.xBar, D.yBar, eval(p), header, vmin=vmin, vmax=vmax,
flux_unbinned=D.unbinned_flux, galaxy=str_plots[i][j],
signal_noise=D.SNRatio, signal_noise_target=SN_target_kin,
ax=axs[i,j], lim_labels=True,
lim_labels_units=units[i][j])
if overplot and p != '' and 'uncert' not in p:
for o, color in overplot.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, axs[i,j], galaxy, nolegend=True, scale=scale)
if i > 0:
if plots[i-1][j] != '' and hasattr(axs[i-1,j], 'ax_dis'):
axs[i-1,j].ax_dis.set_xticklabels([])
axs[i-1,j].ax_dis.set_xlabel('')
if j > 0:
if plots[i][j-1] != '' and hasattr(axs[i,j-1], 'ax_dis'):
axs[i,j].ax_dis.set_yticklabels([])
axs[i,j].ax_dis.set_ylabel('')
else:
axs[i,j].remove()
# age = np.zeros(D2.number_of_bins)
# met = np.zeros(D2.number_of_bins)
# alp = np.zeros(D2.number_of_bins)
# unc_age = np.zeros(D2.number_of_bins)
# unc_met = np.zeros(D2.number_of_bins)
# unc_alp = np.zeros(D2.number_of_bins)
# if not debug:
# for j in xrange(D2.number_of_bins):
# ag, me, al = np.loadtxt('%s/pop/distribution/%i.dat' % (
# vin_dir2, j), unpack=True)
# for plot, unc_plot, pop in zip([age,met,alp],
# [unc_age,unc_met,unc_alp], [ag,me,al]):
# hist = np.histogram(pop, bins=40)
# x = (hist[1][0:-1]+hist[1][1:])/2
# hist = hist[0]
# plot[j] = x[np.argmax(hist)]
# gt_fwhm = hist >= np.max(hist)/2
# unc_plot[j] = np.max(x[gt_fwhm]) - np.min(x[gt_fwhm])
str_plots = ['Age', 'Age uncertainty', 'Metallicity', 'Metallicity uncertainty',
'Alpha enhancement', 'Alpha enhancement\nuncertainty']
units = ['Gyr', 'Gyr', None, None, None, None]
vmin = [0, 0, -2.25, 0, -0.3, 0]
vmax = [15, 2, 0.67, 0.4, 0.5, 0.25]
for j, p in enumerate(['age', 'age.uncert', 'metalicity', 'metalicity.uncert',
'alpha', 'alpha.uncert']):
axs[-1, j] = plot_velfield_nointerp(D2.x, D2.y, D2.bin_num,
D2.xBar, D2.yBar, eval("D2.components['stellar']."+p), header,
vmin=vmin[j], vmax=vmax[j],
# cmap='inferno',
flux_unbinned=D2.unbinned_flux, galaxy=str_plots[j],
signal_noise=D2.SNRatio, signal_noise_target=SN_target_pop,
ax=axs[-1,j], lim_labels=True, lim_labels_units=units[j])
if j > 0:
axs[-1,j].ax_dis.set_yticklabels([])
axs[-1,j].ax_dis.set_ylabel('')
if plots[-1][j] != '' or galaxy in ['ngc0612', 'pks0718-34']:
axs[-2, j].ax_dis.set_xticklabels([])
axs[-2, j].ax_dis.set_xlabel('')
if overplot and 'unc' not in p:
for o, color in overplot.iteritems():
scale = 'log' if o == 'radio' else 'lin'
add_(o, color, axs[-1,j], galaxy, nolegend=True, scale=scale)
for i, a in enumerate(axs.flatten()):
# if galaxy in ['ngc1399', 'pks0718-34'] and i==1:
# break
if hasattr(a, 'ax_dis'):
a.ax_dis.tick_params(top=True, bottom=True, left=True,
right=True, direction='in', which='major', length=20,
width=3, labelsize='large', zorder=10)
a.ax_dis.tick_params(top=True, bottom=True, left=True,
right=True, direction='in', which='minor', length=10,
width=3, zorder=10)
a.ax_dis.xaxis.label.set_size(22)
a.ax_dis.yaxis.label.set_size(22)
# Create gap between pairs of columns
for j in range(1, 6, 2):
for a in axs[:, j].flatten():
ax_loc = a.get_position()
ax_loc.x0 -= 0.007
ax_loc.x1 -= 0.007
a.set_position(ax_loc)
if hasattr(a, 'ax_dis'):
a.ax_dis.set_position(ax_loc)
# for j in range(0, 6, 2):
# for a in axs[:, j+1].flatten():
# ax_loc = a.get_position()
# ax_loc.x0 += 0.01
# ax_loc.x1 += 0.01
# a.set_position(ax_loc)
# if hasattr(a, 'ax_dis'):
# a.ax_dis.set_position(ax_loc)
# fig.text(0.24, 0.9, r'Flux', va='top', ha='center', size='xx-large')
# fig.text(0.51, 0.9, r'Velocty', va='top', ha='center', size='xx-large')
# fig.text(0.8, 0.9, r'Velocty Dispersion', va='top', ha='center',
# size='xx-large')
# Add colorbar
ax_loc = axs[0,-1].get_position()
cax = fig.add_axes([ax_loc.x1+0.03, ax_loc.y0, 0.02, ax_loc.height])
cbar = plt.colorbar(axs[1,1].cs, cax=cax)
cbar.ax.set_yticklabels([])
# plt.show()
fig.savefig('%s/%s.png' % (out_dir, galaxy), bbox_inches='tight',
dpi=200)
import numpy as np
from astropy.io import fits
from checkcomp import checkcomp
cc = checkcomp()
if cc.remote:
import matplotlib # 20160202 JP to stop lack-of X-windows error
matplotlib.use('Agg') # 20160202 JP to stop lack-of X-windows error
import matplotlib.pyplot as plt # used for plotting
else:
import matplotlib.pyplot as plt # used for plotting
from errors2_muse import get_dataCubeDirectory
from classify import get_R_e
from prefig import Prefig
Prefig()
from Bin2 import Data
import disk_fit_functions_binned as dfn
for galaxy in ['ic1459', 'ic4296', 'ngc1316', 'ngc1399']:
print galaxy
f = fits.open(get_dataCubeDirectory(galaxy))
data_file = '%s/Data/muse/analysis/galaxies.txt' % (cc.base_dir)
x_cent_gals, y_cent_gals = np.loadtxt(data_file,
unpack=True,
skiprows=1,
usecols=(1, 2),
dtype=int)
galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str)
i_gal = np.where(galaxy_gals == galaxy)[0][0]
center = (x_cent_gals[i_gal], y_cent_gals[i_gal])
def fit_disk(galaxy, D=None, opt='kin'):
Prefig(subplots=(3, 2))
fig, ax = plt.subplots(2, 3)
f = fits.open(get_dataCubeDirectory(galaxy))
header = f[1].header
f.close()
if D is None:
pickleFile = open('%s/Data/muse/analysis/%s/%s/pickled/dataObj.pkl' %
(cc.base_dir, galaxy, opt))
D = pickle.load(pickleFile)
pickleFile.close()
# Use gas disk
vel = D.components['Hbeta'].plot['vel'].unbinned
vel_err = D.components['Hbeta'].plot['vel'].uncert.unbinned
disk, pars = dfn.disk_fit_exp(vel.copy(),
vel_err.copy(),
sigclip=3.0,
leeway=2.)
ax[1, 0] = plot_velfield_nointerp(
D.x,
D.y,
D.bin_num,
D.xBar,
D.yBar,
D.components['Hbeta'].plot['vel'],
header, #vmin=vmin, vmax=vmax,
flux_unbinned=D.unbinned_flux,
nodots=True,
colorbar=True,
ax=ax[1, 0],
title=r'Gas observed velocity (v$_\mathrm{gas}$)')
ax[1, 1].imshow(disk, cmap=sauron)
ax[1, 1].set_title(r'Gas model velocity (v$_\mathrm{g,mod})$')
plot = D.components['Hbeta'].plot['vel'] - D.rebin(disk,
flux_weighted=True)
# vmin,vmax = set_lims(plot)
ax[1, 2] = plot_velfield_nointerp(
D.x,
D.y,
D.bin_num,
D.xBar,
D.yBar,
plot,
header,
# vmin=vmin, vmax=vmax,
flux_unbinned=D.unbinned_flux,
nodots=True,
colorbar=True,
ax=ax[1, 2],
title=r'Residuals: v$_\mathrm{gas}$ - v$_\mathrm{g,mod}$')
# Use stellar disk
vel = D.components['stellar'].plot['vel'].unbinned
vel_err = D.components['stellar'].plot['vel'].uncert.unbinned
disk, pars = dfn.disk_fit_exp(vel.copy(),
vel_err.copy(),
sigclip=3.0,
leeway=2.)
ax[0, 0] = plot_velfield_nointerp(
D.x,
D.y,
D.bin_num,
D.xBar,
D.yBar,
D.components['stellar'].plot['vel'],
header, #vmin=vmin, vmax=vmax,
flux_unbinned=D.unbinned_flux,
nodots=True,
colorbar=True,
ax=ax[0, 0],
title=r'Stellar observed velocity (v$_\mathrm{\ast}$)')
ax[0, 1].imshow(disk, cmap=sauron)
ax[0, 1].set_title(r'Gas model velocity (v$_\mathrm{\ast,mod}$)')
plot = D.components['Hbeta'].plot['vel'] - D.rebin(disk,
flux_weighted=True)
# vmin,vmax = set_lims(plot)
ax[0, 2] = plot_velfield_nointerp(
D.x,
D.y,
D.bin_num,
D.xBar,
D.yBar,
plot,
header,
# vmin=vmin, vmax=vmax,
flux_unbinned=D.unbinned_flux,
nodots=True,
colorbar=True,
ax=ax[0, 2],
title=r'Residuals: v$_\mathrm{gas}$ - v$_\mathrm{\ast,mod}$')
fig.suptitle('Outflows in %s' % (galaxy))
fig.savefig('%s/Data/muse/analysis/%s/%s/plots/outflows.png' %
(cc.base_dir, galaxy, opt))
return D
def use_kinemetry(gal, opt='pop'):
out_dir = '%s/Data/muse/analysis' % (cc.base_dir)
# colors=['b','y','g']
# fig, ax = plt.subplots()
# pl_ob=[] # array for the plot objects
# missing=0 # number of missing plots (out of 3)
# Plot all avaliable types
# for i, type in enumerate(['flux','vel','sigma']):
# f = '%s/%s/%s/kinemetry/kinemetry_gas_%s.txt' % (out_dir, gal, opt,
# type)
# if os.path.exists(f):
# rad, pa, er_pa, q, er_q, k1, erk1 = np.loadtxt(f, unpack=True,
# skiprows=1)
# pa = rollmed(pa, 5)
# k1 = rollmed(k1, 5)
# # rad*=0.2 # Change to arcsec
# # Align pa[0] as closely as possible with flux pa[0] by +/- 360 deg
# if type == 'flux': pa0 = pa[0]
# else:
# a = np.argmin(np.abs(pa[0]-pa0+[-360,0,360]))
# if a == 0:
# pa -= 360
# elif a == 2:
# pa += 360
# # Optimizing PA
# # cut = np.arange(360,0,-10)
# # r = np.array([list(pa)]*len(cut))
# # for j, c in enumerate(cut):
# # r[j, np.where(r[j,:] > c)[0]] -=360
# # l = np.argmin(np.ptp(r,axis=1))
# # pa = r[l]
# # Finding smoothest pa by add or subtracting 360 deg
# for j in range(1,len(pa)):
# test = np.array([pa[j]-360, pa[j], pa[j]+360])
# a = np.argmin(np.abs(test-pa[j-1]))
# if a==0:
# pa[j:]-=360
# elif a==2:
# pa[j:]+=360
# pl = ax.plot(rad,pa,colors[i],label='%s PA' % (type))
# pl_ob.append(pl)
# # Plot k1 from velocity fit.
# if type == 'vel':
# ax2=ax.twinx()
# b=ax2.plot(rad,k1,'r', label='k1')
# ax2.spines['right'].set_color('red')
# ax2.yaxis.label.set_color('red')
# ax2.tick_params(axis='y', colors='red')
# ax2.set_ylabel('k1',color='r', rotation=270, labelpad=10)
# ax2.spines['left'].set_color('blue')
# else:
# print 'There is no %s KINEMETRY file for %s.' %(type, gal)
# missing +=1
# # If not all missing
# if missing != 3:
# # Get redshift of galaxy from data_file
# data_file = "%s/Data/vimos/analysis/galaxies.txt" % (cc.base_dir)
# classify_file = "%s/Data/muse/analysis/galaxies_classify.txt" % (
# cc.base_dir)
# # different data types need to be read separetly
# z_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1,))
# galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0,),dtype=str)
# i_gal = np.where(galaxy_gals==gal)[0][0]
# z = z_gals[i_gal]
# # Set secondary x axis in kpc.
# ax3=ax.twiny()
# c = 299792 #km/s
# #H = 67.8 #(km/s)/Mpc # From Planck
# H = 70.0 # value used by Bolonga group.
# r_kpc = np.radians(rad/(60.0*60.0)) * z*c/H *1000
# ax3.set_xlim(r_kpc[0],r_kpc[-1])
# ax3.set_xlabel('Radius (kpc)')
# #ax.yaxis.label.set_color('blue')
# #ax.tick_params(axis='y', colors='blue')
# ax.set_ylabel('PA')#,color='b')
# ax.set_xlabel('radius (arcsec)')
# # Mark extent of KDC
# galaxy_gals2, KDC_size = np.loadtxt(classify_file, unpack=True,
# usecols=(0,5), dtype=str, skiprows=1)
# has_KDC = KDC_size!='-'
# galaxy_gals2 = galaxy_gals2[has_KDC]
# KDC_size = KDC_size[has_KDC].astype(float)
# if gal in galaxy_gals2:
# ax.axvline(KDC_size[galaxy_gals2==gal][0])
# # Legend
# try:
# lns = b
# for l in pl_ob: lns+=l
# except UnboundLocalError:
# if missing==1:lns=pl_ob[0]+pl_ob[1]
# else:lns=pl_ob[0]
# labs = [l.get_label() for l in lns]
# ax.legend(lns, labs, loc=0, facecolor='w')
# # Moves title clear of upper x axis
# plt.subplots_adjust(top=0.85)
# ax.set_title('KINEMETRY gas output (smoothed)', y=1.12)
# fig.savefig('%s/%s/%s/kinemetry/kinemetry.png'%(out_dir, gal, opt))
# plt.close()
Prefig(size=(4*16,12), transparent=False)
fig, ax = plt.subplots(1,5)
fig.suptitle('Kinemetry fit to Ionised gas dynamics')
f = fits.open(get_dataCubeDirectory(gal))
header = f[1].header
f.close()
header['NAXIS1'] = 150
header['NAXIS2'] = 150
tessellation_File = "%s/%s/%s/setup/voronoi_2d_binning_output.txt" % (
out_dir, gal, opt)
x,y,bin_num = np.loadtxt(tessellation_File, usecols=(0,1,2),
unpack=True, skiprows=1, dtype=int)
# f = '%s/%s/%s/kinemetry/kinemetry_stellar_vel_2Dmodel.txt' % (out_dir, gal,
# opt)
f = '%s/%s/%s/kinemetry/kinemetry_stellar_vel_2Dmodel.txt' % (out_dir, gal,
'pop')
xbin, ybin, velkin, velcirc = np.loadtxt(f, unpack=True, skiprows=1)
f = '%s/%s/%s/kinemetry/gas_vel.dat' % (out_dir, gal, opt)
vel = np.loadtxt(f, usecols=(0,), unpack=True)
m = np.where(vel!=9999)[0]
# x = [i for j, i in enumerate(x) if bin_num[j] in m]
# y = [i for j, i in enumerate(y) if bin_num[j] in m]
# bin_num = [i for j, i in enumerate(bin_num) if bin_num[j] in m]
# vel = vel[vel != 9999]
vel[vel==9999] = np.nan
velkin_new = vel*0
velcirc_new = vel*0
j = 0
for i in range(len(vel)):
if ~np.isnan(vel[i]):
velkin_new[i] = velkin[j]
velcirc_new[i] = velcirc[j]
j += 1
velkin = velkin_new
velcirc = velcirc_new
velkin[velkin==max(velkin)] = np.nan
velcirc[velcirc==max(velcirc)] = np.nan
# f = '%s/%s/%s/kinemetry/stellar_vel.dat' % (out_dir, gal, opt)
# velkin = np.loadtxt(f, usecols=(0,), unpack=True)
# velkin[velkin==9999] = np.nan
# norm = np.nanmean(vel/velkin)
# norm = 3
# velkin *= norm
# velcirc *= norm
# f = '%s/%s/%s/kinemetry/gas_flux.dat' % (out_dir, gal, opt)
# flux = np.loadtxt(f)
vmin, vmax = set_lims(vel, symmetric=True, positive=False)
plot_velfield_nointerp(x, y, bin_num, xbin, ybin,
vel, header, vmin=vmin, vmax=vmax, nodots=True,
title='Observed Velocity', colorbar=False, ax=ax[0])
plot_velfield_nointerp(x, y, bin_num, xbin, ybin,
velkin, header, vmin=vmin, vmax=vmax, nodots=True,
title='KINEMETRY velocity model', colorbar=False, ax=ax[1])
plot_velfield_nointerp(x, y, bin_num, xbin, ybin,
velcirc, header, vmin=vmin, vmax=vmax, nodots=True,
title='KINEMETRY circluar velocity model', colorbar=False, ax=ax[3])
# vmin, vmax = set_lims(vel-velkin, symmetric=True)
plot_velfield_nointerp(x, y, bin_num, xbin, ybin,
vel-velkin, header, vmin=vmin, vmax=vmax, nodots=True,
title='KINEMETRY model residuals', colorbar=False, ax=ax[2])
# vmin, vmax = set_lims(vel-velcirc, symmetric=True)
plot_velfield_nointerp(x, y, bin_num, xbin, ybin,
vel-velcirc, header, vmin=vmin, vmax=vmax, nodots=True,
title='KINEMETRY circluar model residuals', colorbar=True, ax=ax[4])
for a in ax:
for o, c in {'radio':'g','CO':'c'}.iteritems():
add_(o, c, a, gal, scale='log')#, radio_band='L')
fig.savefig('%s/%s/%s/kinemetry/kinemetry_models.png'%(out_dir, gal, opt))
## Routine to plot the output from use_kinemetry_muse.pro
# Uses a rolling median to smooth the data
from checkcomp import checkcomp
cc=checkcomp()
if 'home' not in cc.device:
import matplotlib # 20160202 JP to stop lack-of X-windows error
matplotlib.use('Agg')
from prefig import Prefig
Prefig(transparent=False)
import numpy as np
import matplotlib.pyplot as plt
import os
from rolling_stats import rollmed
from plot_velfield_nointerp import plot_velfield_nointerp
from astropy.io import fits
from errors2_muse import get_dataCubeDirectory
from plot_results_muse import set_lims, add_
def use_kinemetry(gal, opt='pop'):
out_dir = '%s/Data/muse/analysis' % (cc.base_dir)
# colors=['b','y','g']
# fig, ax = plt.subplots()
# pl_ob=[] # array for the plot objects
# missing=0 # number of missing plots (out of 3)
# Plot all avaliable types
# for i, type in enumerate(['flux','vel','sigma']):
# f = '%s/%s/%s/kinemetry/kinemetry_gas_%s.txt' % (out_dir, gal, opt,
# type)
# if os.path.exists(f):
def BPT(galaxy, D=None, opt='pop', norm="lwv"):
print ' BPT'
Prefig(size=np.array((3, 1)) * 7, transparent=False)
analysis_dir = "%s/Data/muse/analysis" % (cc.base_dir)
galaxiesFile = "%s/galaxies.txt" % (analysis_dir)
x_cent_gals, y_cent_gals = np.loadtxt(galaxiesFile,
unpack=True,
skiprows=1,
usecols=(1, 2),
dtype=int)
galaxy_gals = np.loadtxt(galaxiesFile,
skiprows=1,
usecols=(0, ),
dtype=str)
i_gal = np.where(galaxy_gals == galaxy)[0][0]
center = (x_cent_gals[i_gal], y_cent_gals[i_gal])
# output = '%s/%s/%s' % (analysis_dir, galaxy, opt)
if D is None:
# pickleFile = open('%s/pickled/dataObj.pkl' % (output))
# D = pickle.load(pickleFile)
# pickleFile.close()
D = Data(galaxy, instrument='muse', opt=opt)
output = '%s/%s/%s' % (analysis_dir, galaxy, D.opt)
if D.norm_method != norm:
D.norm_method = norm
D.find_restFrame()
# D.__threshold__ = 3
# ------------=============== BPT diagram =================----------
if all([
l in D.e_components for l in [
'[NII]6583d', '[SII]6716', '[OI]6300d', 'Hbeta', 'Halpha',
'[OIII]5007d'
]
]):
fig, ax = plt.subplots(1, 3, sharey=True)
Prefig(size=(16, 12))
fig2, ax2 = plt.subplots()
r = np.sqrt((D.xBar - center[0])**2 + (D.yBar - center[1])**2)
for i, l in enumerate(['[NII]6583d', '[SII]6716', '[OI]6300d']):
y = np.log10(D.e_line['[OIII]5007d'].flux / 1.35 /
D.e_line['Hbeta'].flux)
x = np.log10(D.e_line[l].flux / D.e_line['Halpha'].flux)
y_err = np.sqrt((D.e_line['[OIII]5007d'].flux.uncert/
D.e_line['[OIII]5007d'].flux)**2 +
(D.e_line['Hbeta'].flux.uncert/D.e_line['Hbeta'].flux)**2)\
/ np.log(10)
x_err = np.sqrt((D.e_line[l].flux.uncert/D.e_line[l].flux)**2 +
(D.e_line['Halpha'].flux.uncert/D.e_line['Halpha'].flux)**2)\
/ np.log(10)
large_err = (x_err > 0.5) + (y_err > 0.5)
Seyfert2_combined = np.ones(len(x)).astype(bool)
LINER_combined = np.ones(len(x)).astype(bool)
SF_combined = np.ones(len(x)).astype(bool)
x_line1 = np.arange(-2.2, 1, 0.001)
if l == '[SII]6716':
Seyfert2 = ((0.72/(x - 0.32) + 1.30 < y) + (x > 0.32)) \
* (1.89 * x + 0.76 < y) * ~large_err
LINER = ((0.72/(x - 0.32) + 1.30 < y) + (x > 0.32)) \
* (1.89 * x + 0.76 > y) * ~large_err
SF = (y < 0.72 / (x - 0.32) + 1.30) * (x < 0.32) * ~large_err
y_line1 = 0.72 / (x_line1 - 0.32) + 1.30
m = y_line1 < 1
ax[i].plot(x_line1[m], y_line1[m], 'k')
y_line2 = 1.89 * x_line1 + 0.76
m = y_line2 > y_line1
a = np.min(x_line1[m])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 1.89 * x_line2 + 0.76
ax[i].plot(x_line2, y_line2, 'k')
lab = r'[SII]$\lambda$$\lambda$6717,6731'
ax[i].set_xlim([-1.2, 0.7])
elif l == '[NII]6583d':
x /= 1.34
Seyfert2 = ((0.61/(x - 0.47) + 1.19 < y) + (x > 0.47)) \
* ~large_err
LINER = ((0.61/(x - 0.47) + 1.19 < y) + (x > 0.47)) \
* ~large_err
SF = (0.61 / (x - 0.47) + 1.19 > y) * (x < 0.47) * ~large_err
y_line1 = 0.61 / (x_line1 - 0.47) + 1.19
m = y_line1 < 1
ax[i].plot(x_line1[m], y_line1[m], 'k')
ax2.plot(x_line1[m], y_line1[m], 'k')
y_line2 = 0.61 / (x_line1 - 0.05) + 1.3
m1 = y_line2 < y_line1
a = np.min(x_line1[m1])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 0.61 / (x_line2 - 0.05) + 1.3
m2 = y_line2 < 1
ax[i].plot(x_line2[m2], y_line2[m2], 'k--')
ax2.plot(x_line2[m2], y_line2[m2], 'k--')
lab = r'[NII]$\lambda$6584'
ax[i].set_xlim([-2, 1])
ax2.set_xlim([-2, 1])
ax2.set_ylim([-1.2, 1.5])
distance = np.zeros(len(x))
for j in range(len(distance)):
distance[j] = np.sqrt(
np.min((x_line1[m] - x[j])**2 +
(y_line1[m] - y[j])**2))
limit = 1 # if distance is greater than limit then consider
# it completely in it's region.
distance[distance > limit] = limit
distance[SF] *= -limit
try:
ax2.scatter(x,
y,
c=distance,
cmap=sauron,
vmin=-limit,
vmax=limit)
except:
print 'This did not work cotton. Galaxy: %s' % (galaxy)
ax2.set_ylabel(r'log([OIII]/$H_\beta$)')
ax2.set_xlabel(r'log(%s/$H_\alpha$)' % (lab))
add_grids(ax[i], '[NII]', '[OIII]', x_Ha=True)
elif l == '[OI]6300d':
x /= 1.33
Seyfert2 = ((y > 0.73/(x + 0.59) + 1.33) + (x > -0.59)) \
* (y > 1.18 * x + 1.30) * ~large_err
LINER = ((y > 0.73/(x + 0.59) + 1.33) + (x > -0.59)) \
* (y < 1.18 * x + 1.30) * ~large_err
SF = (y < 0.73 / (x + 0.59) + 1.33) * (x < -0.59) * ~large_err
y_line1 = 0.73 / (x_line1 + 0.59) + 1.33
m = y_line1 < 1
ax[i].plot(x_line1[m], y_line1[m], 'k')
y_line2 = 1.18 * x_line1 + 1.30
m = y_line2 > y_line1
a = np.min(x_line1[m])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 1.18 * x_line2 + 1.30
ax[i].plot(x_line2, y_line2, 'k')
ax[i].axvline(-0.59, ls='--', c='k')
lab = r'[OI]$\lambda$6300'
ax[i].set_xlim([-2.2, 0])
add_grids(ax[i], '[OI]', '[OIII]', x_Ha=True)
ax[i].set_ylim([-1.2, 1.5])
Seyfert2_combined *= Seyfert2
LINER_combined *= LINER
SF_combined *= SF
myerrorbar(ax[i],
x,
y,
xerr=x_err,
yerr=y_err,
marker='.',
color=r)
# ax[i].errorbar(x[LINER], y[LINER], yerr=y_err[LINER],
# xerr=x_err[LINER], c='g', fmt='.')
# ax[i].errorbar(x[Seyfert2], y[Seyfert2], yerr=y_err[Seyfert2],
# xerr=x_err[Seyfert2], c='r', fmt='.')
# ax[i].errorbar(x[SF], y[SF], yerr=y_err[SF], xerr=x_err[SF],
# c='b', fmt='.')
ax[0].set_ylabel(r'log [OIII]$\lambda$5007/H$\,\beta$')
ax[i].set_xlabel(r'log(%s/$H_\alpha$)' % (lab))
saveTo = '%s/plots/BPT.png' % (output)
if not os.path.exists(os.path.dirname(saveTo)):
os.makedirs(os.path.dirname(saveTo))
fig.subplots_adjust(wspace=0) #,hspace=0.01)
fig.savefig(saveTo, dpi=100)
fig2.savefig('%s/plots/BPT2.png' % (output), dpi=100)
plt.close()
Prefig(size=(16, 12), transparent=False)
# ------------================= BPT map ===================----------
m = np.ones(D.number_of_bins) * np.nan
m[Seyfert2_combined] = +1
m[LINER_combined] = 0
m[SF_combined] = -1
f = fits.open(get_dataCubeDirectory(galaxy))
header = f[1].header
f.close()
saveTo = '%s/plots/AGN_map.png' % (output)
ax = plot_velfield_nointerp(D.x,
D.y,
D.bin_num,
D.xBar,
D.yBar,
m,
header,
vmin=-1.3,
vmax=1.3,
nodots=True,
title='Map of AGN',
save=saveTo,
res=0.2,
flux_unbinned=D.unbinned_flux,
center=center)
add_('radio', 'r', ax, galaxy)
plt.close()
# ------------================= BPT map2 ===================----------
Prefig(size=(16, 12), transparent=False)
f = fits.open(get_dataCubeDirectory(galaxy))
header = f[1].header
f.close()
saveTo = '%s/plots/AGN_map2.png' % (output)
ax = plot_velfield_nointerp(D.x,
D.y,
D.bin_num,
D.xBar,
D.yBar,
distance,
header,
vmin=-1.3,
vmax=1.3,
nodots=True,
title='Map of AGN',
save=saveTo,
res=0.2,
flux_unbinned=D.unbinned_flux,
center=center)
ax.saveTo = saveTo
add_('radio', 'r', ax, galaxy)
plt.close()
# ------------============== WHaN2 diagram ================----------
if all([l in D.e_components for l in ['[NII]6583d', 'Halpha']]):
Prefig(size=(16, 12 * 3), transparent=False)
fig, ax = plt.subplots(3)
x = np.log10(D.e_line['[NII]6583d'].flux / D.e_line['Halpha'].flux)
y = np.log10(D.e_line['Halpha'].equiv_width)
Ha_Hapeak = D.e_line['Halpha'].flux / np.nanmax(
D.e_line['Halpha'].flux)
p1 = Ha_Hapeak <= 0.2
p2 = Ha_Hapeak > 0.2
c = np.array(np.sqrt((D.xBar - center[0])**2 +
(D.yBar - center[1])**2))
passive = (D.e_line['Halpha'].equiv_width <
0.5) * (D.e_line['[NII]6583d'].equiv_width < 0.5)
ax[0].scatter(x, y, c=c)
ax[0].scatter(x[passive], y[passive], marker='x', c='r')
xlim = ax[0].get_xlim()
ylim = ax[0].get_ylim()
xlim = [-2, 0.75]
ylim = [-1, 1.2]
ax[1].scatter(x[p1], y[p1], c=c[p1], vmin=np.min(c), vmax=np.max(c))
ax[1].scatter(x[p1 * passive], y[p1 * passive], marker='x', c='r')
ax[1].text(-1.9, -0.8,
r'0 $<$ H$_\alpha$/H$_\alpha^{\mathrm{peak}}$ $\leq$ 0.2')
ax[2].scatter(x[p2], y[p2], c=c[p2], vmin=np.min(c), vmax=np.max(c))
ax[2].scatter(x[p2 * passive], y[p2 * passive], marker='x', c='r')
ax[2].text(-1.9, -0.8,
r'0.2 $<$ H$_\alpha$/H$_\alpha^{\mathrm{peak}}$ $\leq$ 1')
# Dianotics lines from R Cid Fernandes et.al. 2011 MNRAS 413 1687
for a in ax:
a.axhline(np.log10(3), ls=':', c='k') #
a.plot([-0.4, -0.4], [np.log10(3), ylim[1]], ls=':', c='k')
a.plot([-0.4, xlim[1]], [np.log10(6), np.log10(6)], ls=':', c='k')
a.set_xlim(xlim)
a.set_ylim(ylim)
a.set_ylabel(r'log(EW(H$_\alpha$)/$\AA$)')
a.set_xlabel(r'log([NII]/H$_\alpha$)')
a.text(-1.9, 1, 'Star Forming')
a.text(-0.35, 1, 'strong AGN')
a.text(-0.35, 0.55, 'weak AGN')
a.text(-1.9, 0.25, 'Retired Galaxies')
fig.suptitle('WHaN2 plot')
fig.savefig('%s/plots/WHaN2.png' % (output))
plt.close()
# ------------=============== MEx diagram =================----------
if all([l in D.e_components for l in ['[OIII]5007d', 'Hbeta']]):
Prefig()
# from Atlas3D XXXI (Section 6.2.1)
fig, ax = plt.subplots()
y = np.log10(D.e_line['[OIII]5007d'].flux / 1.35 /
D.e_line['Hbeta'].flux)
y_err = y * np.sqrt((D.e_line['[OIII]5007d'].flux.uncert /
D.e_line['[OIII]5007d'].flux)**2 +
(D.e_line['Hbeta'].flux.uncert /
D.e_line['Hbeta'].flux)**2) / np.log(10)
large_err = y_err**2 > 1
m = ~large_err * (D.e_line['[OIII]5007d'].equiv_width < 0.8)
ax.errorbar(D.components['stellar'].plot['sigma'][m],
y[m],
c='b',
xerr=D.components['stellar'].plot['sigma'].uncert[m],
yerr=y_err[m],
fmt='.',
label='EW([OIII]) < 0.8')
m = ~large_err * (D.e_line['[OIII]5007d'].equiv_width >= 0.8)
ax.errorbar(D.components['stellar'].plot['sigma'][m],
y[m],
c='r',
xerr=D.components['stellar'].plot['sigma'].uncert[m],
yerr=y_err[m],
fmt='.',
label=r'EW([OIII]) $\ge 0.8$')
ax.legend(facecolor='w')
x_line = np.arange(70, 1000, 1)
y_line = 1.6 * 10**-3 * x_line + 0.33
ax.plot(x_line, y_line, c='k')
ax.set_xlim(
[0, min(max(D.components['stellar'].plot['sigma'][m]), 500)])
ax.set_ylim([-1.2, 1.5])
ax.axvline(70, c='k')
ax.axhline(np.log10(0.5),
xmin=70. /
min(max(D.components['stellar'].plot['sigma'][m]), 500),
c='k')
ax.axhline(np.log10(1),
xmin=70. /
min(max(D.components['stellar'].plot['sigma'][m]), 500),
c='k')
ylim = ax.get_ylim()
yrange = ylim[1] - ylim[0]
ax.text(60, ylim[0] + 0.96 * yrange, 'SF')
ax.text(75, 0.55, 'Seyfert 2')
ax.text(75, 0.15, 'LINER')
ax.text(75, -0.23, 'Transition')
ax.set_xlabel(r'$\sigma_\ast$')
ax.set_ylabel(r'log [OIII]$\lambda$5007/H$\,\beta$')
ax.set_title('Mass-excitation (MEx) diagnotics for %s' %
(galaxy.upper()))
fig.savefig('%s/plots/MEx.png' % (output))
# ------------============== SAURON diagram ===============----------
if all([l in D.e_components for l in ['[NI]d', 'Hbeta', '[OIII]5007d']]):
# from SAURON XVI.
fig, ax = plt.subplots()
# y and y_err as MEx above
x = np.log10(D.e_line['[NI]d'].flux / D.e_line['Hbeta'].flux)
x_err = np.sqrt(
(D.e_line['[NI]d'].flux.uncert / D.e_line['[NI]d'].flux)**2 +
(D.e_line['Hbeta'].flux.uncert /
D.e_line['Hbeta'].flux)**2) / np.log(10)
ax.errorbar(x, y, xerr=x_err, yerr=y_err, fmt='.')
# Add BPT line - transformed to NI
xlim = np.array([-2.5, 0.5])
x_line = np.linspace(xlim[0], xlim[1], 100)
y_line = 0.61 / (x_line - 0.47) + 1.19
m = y_line < 1
plt.plot(x_line[m] - lnd + lbd, y_line[m], 'k')
y_line2 = 0.61 / (x_line1 - 0.05) + 1.3
m1 = y_line2 < y_line1
a = np.min(x_line1[m1])
x_line2 = np.arange(a, 0.60, 0.001)
y_line2 = 0.61 / (x_line2 - 0.05) + 1.3
m2 = y_line2 < 1
ax.plot(x_line2[m2] - lnd + lbd, y_line2[m2], 'k--')
ax.set_xlim([-2.5, 0.5])
ax.set_ylim([-1.5, 1.5])
ax.set_xlabel(r'log [NI]$\lambda\lambda$5197,5200/H$\,\beta$')
ax.set_ylabel(r'log [OIII]$\lambda$5007/H$\,\beta$')
ax.set_title('SAURON diagnotics for %s' % (galaxy.upper()))
add_grids(ax, '[NI]', '[OIII]')
fig.savefig('%s/plots/SAURON_diagnoistic.png' % (output))
plt.close()
# ------------=========== Hbeta flux profile ===============----------
if 'Hbeta' in D.components.keys():
Prefig()
fig, ax = plt.subplots()
Hb = D.e_line['Hbeta'].flux
myerrorbar(ax, r, Hb, yerr=Hb.uncert,
color=np.log10(D.e_line['[OIII]5007d'].flux/1.35\
/ D.e_line['Hbeta'].flux).clip(-1,1))
o = np.argsort(r)
# ax.plot(r[o][1:], Hb[np.argmin(np.abs(r-1))] * r[o][1:]**-2, 'k')
ax.plot(
r[o],
np.nanmedian(r[o][np.isfinite(Hb[o])])**2 *
np.nanmedian(Hb[o][np.isfinite(Hb[o])]) * r[o]**-2, 'k')
ax.set_yscale('log')
ax.set_ylabel(r'H$_\beta$ Flux')
ax.set_xlabel('Radius (pixels)')
fig.savefig('%s/plots/Hbeta_profile.png' % (output))
plt.close(fig)
return D
matplotlib.use('Agg') # 20160202 JP to stop lack-of X-windows error
import matplotlib.pyplot as plt # used for plotting
else:
import matplotlib.pyplot as plt # used for plotting
import cPickle as pickle
from plot_velfield_nointerp import plot_velfield_nointerp
import numpy as np
import os
from plot_results_muse import set_lims, add_
from errors2_muse import get_dataCubeDirectory, set_params, run_ppxf
from pop import get_absorption, population
from classify import get_R_e
from errors2 import apply_range
from astropy.io import fits
from prefig import Prefig
Prefig(size=(16 * 2, 12 * 3), transparent=False)
def plot_stellar_pop(galaxy,
method='median',
opt='pop',
D=None,
overplot={},
gradient=True):
print 'Plotting stellar population'
if cc.device == 'glamdring':
vin_dir = '%s/analysis_muse/%s/%s/pop' % (cc.base_dir, galaxy, opt)
data_file = '%s/analysis_muse/galaxies.txt' % (cc.base_dir)
else:
vin_dir = '%s/Data/muse/analysis/%s/%s/pop' % (cc.base_dir, galaxy,
