def bigplots(agb_tracks, infile):
if type(agb_tracks[0]) == str:
agb_tracks = [fileIO.get_numeric_data(a) for a in agb_tracks]
out_fig = os.path.join(infile.diagnostic_dir, 'hrd_%.4f.png' % agb_tracks[0].metallicity)
plot_title = '$\dot{M}_{\\rm M13}\ Z=%.4f$' % agb_tracks[0].metallicity
nagb_tracks = len(agb_tracks)
fig, (axs) = rspg.setup_multiplot(nagb_tracks,
ylabel='$\log\ L\ (L_\odot)$',
xlabel='$\log\ T_{\\rm eff}\ (K)$',
title =plot_title,
subplots_kwargs={'figsize': (30,30),
'squeeze': True})
axs = axs.flatten()
[hrd_slopes(agb_tracks[i], ax=axs[i]) for i in range(nagb_tracks)]
plt.savefig(out_fig, dpi=300)
ylabel = ['$C/O$', '$\log\ L\ (L_\odot)$', '$\log\ T_{\\rm eff}\ (K)$']
ycol = ['C/O', 'logl', 'logt']
for i in range(len(ylabel)):
fig, (axs) = rspg.setup_multiplot(nagb_tracks,
ylabel=ylabel[i],
xlabel='${\\rm Age (yr)}$',
title =plot_title,
subplots_kwargs={'figsize': (30,30),
'squeeze': True})
axs = axs.flatten()
[age_vs_plot(agb_tracks[j], infile, ycol=ycol[i], ax=axs[j], annotate=True,
xlabels=False, ylabels=False, save_plot=False)
for j in range(len(agb_tracks))]
out_fig = out_fig.replace('hrd', 'age_v_%s' % ycol[i].lower().replace('c/o',
'co'))
plt.savefig(out_fig, dpi=300)
def plot_integrated_colors(filenames, labels='Z'):
if type(filenames) is str:
filenames = [filenames]
ax = None
cols = ['k']
else:
fig, ax = plt.subplots()
cols = brewer2mpl.get_map('Spectral', 'Diverging',
len(filenames)).mpl_colors
if labels == 'Z':
fmt = '$Z=%.4f$'
labels = [fmt % float(l.replace('.dat', '').split('Z')[1])
for l in filenames]
else:
print 'need to fix labels'
labels = [''] * len(filenames)
for i, filename in enumerate(filenames):
data = rsp.fileIO.readfile(filename)
ycol = 'V-K'
xcol = 'Age'
ax = rg.color_color(data, xcol, ycol, xscale='log', ax=ax,
plt_kw={'lw': 2, 'color': cols[i],
'label': labels[i]})
plot_cluster_data(ax)
ax.legend(frameon=False, loc=0, numpoints=1)
ax.set_xlabel(r'${\rm %s}$' % xcol, fontsize=20)
ax.set_ylabel(r'${\rm %s}$' % ycol, fontsize=20)
plt.tick_params(labelsize=16)
return ax
def plot_mc_integrated_colors(filenames):
fig, ax = plt.subplots()
for i, filename in enumerate(filenames):
data = rsp.fileIO.readfile(filename)
ycol = 'V-K'
xcol = 'Age'
ax = rg.color_color(data, xcol, ycol, xscale='log', ax=ax,
plt_kw={'marker': '.', 'color': 'k',
'alpha': 0.3})
plot_cluster_data(ax)
def test_ycen(et):
import ResolvedStellarPops.graphics.GraphicsUtils as rspg
eeps = ['YCEN_0.000', 'YCEN_0.100', 'YCEN_0.200', 'YCEN_0.400',
'YCEN_0.500', 'YCEN_0.550']
cols = rspg.discrete_colors(len(eeps))
ptcri_inds_kw = {'hb': False, 'sandro': False}
add_eep_inds([et], *eeps, **ptcri_inds_kw)
fig, ax = plt.subplots(figsize=(8, 8))
for i, track in enumerate(et.tracks):
if track.mass == 0.5 or track.mass == 0.55:
continue
tinds = track.ptcri.inds_between_ptcris('YCEN_0.550', 'YCEN_0.000',
sandro=False)
inds = np.array([et.__getattribute__(s) for s in et.__dict__.keys() if s.startswith('ycen')]).T
if np.sum(inds) == 0:
continue
#ax.plot(track.data.LOG_TE, track.data.LOG_L, color='black', alpha=0.1)
ax.plot(track.data.LOG_TE[tinds], track.data.LOG_L[tinds], color='red')
[ax.plot(track.data.LOG_TE[inds[i][j]], track.data.LOG_L[inds[i][j]], 'o', color=cols[j]) for j in range(len(eeps))]
ax.text(track.data.LOG_TE[inds[i][0]], track.data.LOG_L[inds[i][0]], '%.3g' % track.mass)
ax.set_title('Z=%.4f' % track.Z)
def vary_the_SFH(self, cmd_input_file, prep_tri_kw=None, make_many_kw=None,
dry_run=False, diag_plots=False):
tri_sfr_fmt = os.path.join(self.outfile_loc, 'tri_%s' % self.prefix)
tri_sfr_fmt += '_003i.sfr'
(sfr_out_file, object_mass) = zip(*[self._make_trilegal_sfh(outfile=tri_sfr_fmt % i, **make_many_kw)
for i in range(nsfhs)])
self.sfr_files = list(sfr_out_file)
prep_tri_kw = dict({'loidl': True, photsys: '2mass'}.items() + prep_tri_kw.items())
self.galaxy_inputs = [self.prepare_trilegal(tri_sfr_file=self.sfr_files[i], object_mass[i],
**prep_tri_kw)
for i in range(len(nsfhs))
output = ADKLJSFL:KAJD
# set up the trilegal run and then test!!!
rsp.TrilegalUtils.run_trilegal(cmd_input_file, self.galaxy_input[i], output,
loud=True)
def load_raw_vmc_data():
#photom_file = research_path + 'TP-AGBcalib/LMC_Calib/photom.dat'
#photom = rsp.fileIO.readfile(photom_file)
results_file = research_path + 'TP-AGBcalib/LMC_Calib/photom_model.dat'
dtype = [('RAJ2000', '<f8'), ('DEJ2000', '<f8'), ('recno', '<f8'),
('Seq', '<f8'), ('tau', '<f8'), ('logML', '<f8'), ('Lsun', '<f8'),
('Cl', '|S4'), ('Q', '|S1'), ('P', '|S1'), ('SED', '|S3'),
('recno1', '<f8'), ('Seq1', '<f8'), ('Umag', '<f8'),
('Bmag', '<f8'), ('Vmag', '<f8'), ('Imag', '<f8'),
('YmagV', '<f8'), ('JmagV', '<f8'), ('KsmagV', '<f8'),
('Jmag2', '<f8'), ('Hmag2', '<f8'), ('Ksmag2', '<f8'),
('[3.6]1', '<f8'), ('[3.6]2', '<f8'), ('[4.5]1', '<f8'),
('[4.5]2', '<f8'), ('[5.8]1', '<f8'), ('[5.8]2', '<f8'),
('[8.0]1', '<f8'), ('[8.0]2', '<f8'), ('[24]1', '<f8'),
('[24]2', '<f8'), ('chi2C', '<f8'), ('chi2O', '<f8')]
results = np.genfromtxt(results_file, dtype=dtype)
return results
def read_vmc_table(filename):
dtype = [('DEJ2000d', '<f8'),
('RAJ2000d', '<f8'),
('num', '<f8'),
('seq', '<f8'),
('RAJ2000', '|S12'),
('DEJ2000', '|S12'),
('tau', '<f8'),
('logMdot', '<f8'),
('Lum', '<f8'),
('Cl', '|S4'),
('Q', '|S1'),
('P', '|S1'),
('SED', '|S3'),
('Umag', '<f8'),
('Bmag', '<f8'),
('Vmag', '<f8'),
('Imag', '<f8'),
('YmagV', '<f8'),
('JmagV', '<f8'),
('KsmagV', '<f8'),
('Jmag2', '<f8'),
('Hmag2', '<f8'),
('Ksmag2', '<f8'),
('u3.6_1', '<f8'),
('u3.6_2', '<f8'),
('u4.5_1', '<f8'),
('u4.5_2', '<f8'),
('u5.8_1', '<f8'),
('u5.8_2', '<f8'),
('u8.0_1', '<f8'),
('u8.0_2', '<f8'),
('u24_1', '<f8'),
('u24_2', '<f8'),
('chi2C', '<f8'),
('chi2O', '<f8')]
return np.genfromtxt(filename, dtype=dtype)
def read_lmc_cat(filename):
with open(filename, 'r') as f:
header = f.readline()
col_keys = header.replace('#', '').strip().split()
return np.genfromtxt(filename, names=col_keys)
def make_plot(output, extra='', photsys='2mass', tpagb_mass_bins=None):
if tpagb_mass_bins is None:
tpagb_mass_bins = np.arange(1., 6, 0.5)
filter1 = 'J'
if 'ubv' in photsys:
filter2 = 'K'
elif '2mass' in photsys:
filter2 = 'Ks'
sgal = rsp.Galaxies.simgalaxy(output, filter1=filter1, filter2=filter2)
sgal.all_stages('TPAGB')
sgal.mix_modelname(sgal.name)
# http://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=J/A+A/537/A105
#vmcagbs = research_path + 'TP-AGBcalib/LMC_Calib/VMCAGBS_fmt.dat'
#data = read_vmc_table(vmcagbs)
vmcagbs = research_path + 'TP-AGBcalib/LMC_Calib/VMCAGBS_fmt2.dat'
gal = rsp.Galaxies.galaxy(vmcagbs, filter1='Jmag2', filter2='Ksmag2', hla=False,
angst=False)
gal.filters = [filter1, filter2]
#gal.maglims = [13., 12.]
gal.target = sgal.mix
smg = rsp.Galaxies.sim_and_gal(gal, sgal)
outfile = output.replace('.dat','_%s.png' % extra)
fig, axs, top_axs = smg.make_LF(filter1, filter2, color_hist=False,
plot_tpagb=True , add_boxes=False,
plot_LF_kw={'xlim': (0.5, 4)},
figname=outfile)
axs[1].cla()
ax = sgal.color_by_arg(0,0,0, xdata=sgal.color, ydata=sgal.mag2,
coldata=sgal.data.get_col('m_ini'),
bins=tpagb_mass_bins, slice_inds=sgal.itpagb,
ax=axs[1], xlim=axs[0].get_xlim(),
ylim=axs[0].get_ylim(), fig=fig)
tpagb_masses = sgal.data.get_col('m_ini')[sgal.itpagb]
tpinds = np.digitize(tpagb_masses, tpagb_mass_bins)
tpinds = tpinds[tpinds < len(tpagb_mass_bins)]
tpagb_mass_inds = [sgal.itpagb[tpinds==i] for i in np.unique(tpinds)]
# some masses are not recovered.
tpagb_mass_bins = tpagb_mass_bins[np.unique(tpinds)]
if 3 <= len(tpagb_mass_bins) <= 11:
bmap = brewer2mpl.get_map('Paired', 'Qualitative', len(tpagb_mass_bins))
cols = bmap.mpl_colors
else:
cols = rspg.discrete_colors(len(tpagb_mass_bins), colormap='RdYlGn')
for i, tpagb_mass_ind in enumerate(tpagb_mass_inds):
hist, _ = np.histogram(sgal.mag2[tpagb_mass_ind], bins=smg.bins)
axs[2].semilogx(hist, smg.bins[1:], color=cols[i], ls='steps', lw=2)
fig1, ax1 = plt.subplots()
tpagb_imasses = sgal.data.get_col('m_ini')[sgal.itpagb]
tpagb_amasses = sgal.data.get_col('Mact')[sgal.itpagb]
dm = tpagb_imasses-tpagb_amasses
ax1.plot(dm, sgal.mag2[sgal.itpagb], '.')
outfile = outfile.replace('%s.png' % extra, '%s_by_mass.png' % extra)
plt.savefig(outfile, dpi=300, bbox_to_inches='tight')
print 'wrote %s' % outfile
return sgal
def cslf(sgals, outfile=None):
'''
A simple CSLF plot.
Takes a list of rsp.simgalaxy types.
This takes the mag to be Mbol, and uses the first line of the trilegal
catalogue to get dmod and Av.
If an outfile is specified, will save fig.
'''
#sgals = [rsp.Galaxies.simgalaxy(tri_out, filter1='J', filter2='Ks',
# photsys='2mass') for tri_out in tri_outs]
[sgal.load_ic_mstar() for sgal in sgals]
bmap = brewer2mpl.get_map('Set1', 'Qualitative', 3)
cols = bmap.mpl_colors
bins = np.arange(-6.5, -2.5, 0.15)
fig, ax = plt.subplots(figsize=(8, 8))
for i, sgal in enumerate(sgals):
model = sgal.name.split('_')[-1].split('.dat')[0]
lab = galaxy_tests.translate_model_name(model) + ', $N=%i$' % (len(sgal.icstar))
Mbol = sgal.data.get_col('mbol') - sgal.data.get_col('m-M0')[0] - sgal.data.get_col('Av')[0]
plt.hist(Mbol[sgal.icstar], bins=bins, histtype='step', lw=5-i, color='white')
plt.hist(Mbol[sgal.icstar], bins=bins, histtype='step', label=lab, lw=4-i, color=cols[i])
ax.legend(frameon=False, prop={'size': 16}, loc=0)
ax.set_xlim()[::-1]
ax.set_xlabel('$M_{\\rm bol}$', fontsize=20)
ax.set_ylabel('$N_c$', fontsize=20)
plt.tick_params(labelsize=16)
if outfile is not None:
plt.savefig(outfile, dpi=300)
return
def color_mdot_plot(sgals):
nmodels = len(sgals)
bmap = brewer2mpl.get_map('Set1', 'Qualitative', nmodels)
cols = bmap.mpl_colors
results = load_raw_vmc_data()
data_color = results['Jmag2'] - results['Ksmag2']
data_logML = results['logML']
data_icstar = np.nonzero(results['Cl'] == 'C')
data_imstar = np.nonzero(results['Cl'] == 'O')
fig, (axs) = plt.subplots(nrows=nmodels, sharex=True, sharey=True, figsize=(8,8))
for i, sgal in enumerate(sgals):
model = sgal.name.split('_')[-1].split('.dat')[0]
lab = galaxy_tests.translate_model_name(model)
logml = sgal.data.get_col('logML')
color = sgal.data.get_col('J') - sgal.data.get_col('Ks')
axs[i].plot(color[sgal.icstar], logml[sgal.icstar], '.', label=lab, color=cols[i])
axs[i].plot(color[sgal.imstar], logml[sgal.imstar], 'x', label=lab, color=cols[i])
axs[i].plot(data_color[data_icstar], data_logML[data_icstar], '.', color='k')
axs[i].plot(data_color[data_imstar], data_logML[data_imstar], 'x', color='k')
axs[i].text(0.85, 0.1, lab, transform=axs[i].transAxes, fontsize=16)
axs[1].set_ylabel('$\log \dot{M}\ ({\\rm M_\odot/yr})$', fontsize=20)
axs[0].set_ylim(-11, -4)
axs[0].yaxis.set_major_locator(MultipleLocator(2))
axs[0].yaxis.set_minor_locator(MultipleLocator(.5))
axs[0].xaxis.set_minor_locator(MultipleLocator(.2))
axs[2].set_xlabel('$J-Ks$', fontsize=20)
plt.tick_params(labelsize=16)
fig.savefig('mdot_jks.png', dpi=300)
return
def main():
overwrite = True
loidl = [True, False]
photsys = '2mass'
extra = ''
for i in range(len(loidl)):
if loidl is True:
extra = '_loidl_%s' % photsys
else:
extra = '_%s' % photsys
outputs = make_trilegal_sim(loidl=loidl[i],
photsys=photsys,
overwrite=overwrite,
extra=extra)
sgals = [make_plot(output, extra=extra) for output in outputs]
outfile = os.path.join(os.path.split(output)[0], 'cslf%s.png' % extra)
cslf(sgals, outfile=outfile)
if __name__ == "__main__":
import pdb
pdb.set_trace()
main()
def color_by_arg(bins=None,
cmap=None,
ax=None,
fig=None,
labelfmt='$%.3f$',
xdata=None,
ydata=None,
coldata=None,
xlim=None,
ylim=None,
slice_inds=None,
legend=True,
filter1=None,
filter2=None,
ymag=None):
if fig is None:
fig = plt.figure()
if ax is None:
ax = plt.axes()
if bins is None:
bins = 10
if slice_inds is not None:
xdata = xdata[slice_inds]
ydata = ydata[slice_inds]
coldata = coldata[slice_inds]
# need the bins to be an array to use digitize.
if type(bins) == int:
hist, bins = np.histogram(coldata, bins=bins)
inds = np.digitize(coldata, bins)
uinds = np.unique(inds)
# digitize sticks all points that aren't in bins in the final bin
# cut that bin, or plot will be meaningless..
if uinds[-1] == len(bins):
uinds = uinds[:-1]
if cmap is None:
if 3 <= len(uinds) <= 11:
#bmap = brewer2mpl.get_map('Spectral', 'Diverging', len(uinds))
bmap = brewer2mpl.get_map('Paired', 'Qualitative', len(uinds))
cols = bmap.mpl_colors
else:
cols = rg.discrete_colors(len(uinds), colormap='RdYlGn')
else:
cols = rg.discrete_colors(len(uinds), colormap=cmap)
sub_inds = np.array([])
nc = len(cols[0])
colors = np.ndarray(shape=(len(xdata), nc), dtype=float)
labs = []
for j, i in enumerate(uinds):
sinds, = np.nonzero(inds == i)
N = len(sinds)
if N == 0:
continue
if labelfmt != '':
labs.append(labelfmt % bins[i]) # bins are left bin edges.
colors[sinds] = cols[j]
sub_inds = np.append(sub_inds, sinds)
inds = map(int, sub_inds[:])
np.random.shuffle(inds)
ax.scatter(xdata[inds],
ydata[inds],
marker='o',
s=15,
edgecolors='none',
color=colors[inds])
if xlim is None:
xlim = (xdata.min(), xdata.max())
if ylim is None:
ylim = (ydata.max(), ydata.min())
ax.set_xlim(xlim)
ax.set_ylim(ylim)
if ymag is None:
ymag = filter2
if filter1 is not None and filter2 is not None:
ax.set_xlabel('$%s-%s$' % (filter1, filter2), fontsize=20)
ax.set_ylabel('$%s$' % ymag, fontsize=20)
ax.tick_params(labelsize=16)
if legend is True:
# fake out the legend...
[
ax.plot(999, 999, 'o', color=cols[i], mec=cols[i], label=labs[i])
for i in range(len(labs))
]
ax.legend(loc=0, numpoints=1, frameon=False)
return fig, ax