def make_wtvsparamval(self, allparams=True, paramlist=None):
'''
template usage vs parameter value
'''
if allparams:
paramlist = set(self.test_metadata.colnames) & set(pca.metadata.colnames)
paramlist = [n for n in paramlist
if self.test_metadata[n].shape == (len(self.test_metadata), )]
paramlist = sorted(paramlist)
gs, fig = figures_tools.gen_gridspec_fig(
len(paramlist), border=(1., 1., 0.5, 0.5), space=(0.7, 0.4),
spsize=(2.5, 1.25))
subplot_inds = iterprod(range(gs._nrows), range(gs._ncols))
fig_axes = {n: fig.add_subplot(gs[ii, jj])
for (ii, jj), n in zip(subplot_inds, paramlist)}
for n, ax in fig_axes.items():
ax.hist(self.trn_metadata[n].data, weights=self.trn_usage_cts,
histtype='step', density=True)
ax.set_yscale('log')
ax.set_ylabel(r'density', size='x-small')
ax.tick_params(labelsize='x-small')
ax.set_xlabel(n.replace('_', '\_'))
figures_tools.savefig(fig, fname='wtsvsparams.png', fdir=self.workdir)
def make_wtvsparamval(self, allparams=True, paramlist=None):
'''
template usage vs parameter value
'''
if allparams:
paramlist = set(self.test_metadata.colnames) & set(
pca.metadata.colnames)
paramlist = [
n for n in paramlist
if self.test_metadata[n].shape == (len(self.test_metadata), )
]
paramlist = sorted(paramlist)
gs, fig = figures_tools.gen_gridspec_fig(len(paramlist),
border=(1., 1., 0.5, 0.5),
space=(0.7, 0.4),
spsize=(2.5, 1.25))
subplot_inds = iterprod(range(gs._nrows), range(gs._ncols))
fig_axes = {
n: fig.add_subplot(gs[ii, jj])
for (ii, jj), n in zip(subplot_inds, paramlist)
}
for n, ax in fig_axes.items():
ax.hist(self.trn_metadata[n].data,
weights=self.trn_usage_cts,
histtype='step',
density=True)
ax.set_yscale('log')
ax.set_ylabel(r'density', size='x-small')
ax.tick_params(labelsize='x-small')
ax.set_xlabel(n.replace('_', '\_'))
figures_tools.savefig(fig, fname='wtsvsparams.png', fdir=self.workdir)
def make_cosmic_sfr(CSP_dir):
SFHs_fnames = glob.glob(os.path.join(CSP_dir, 'SFHs_*.fits'))
nsubpersfh = fits.getval(SFHs_fnames[0], ext=0, keyword='NSUBPER')
SFHs = np.row_stack(
[fits.getdata(fn_, 'allsfhs') / fits.getdata(fn_, 'mformed')[::nsubpersfh, None]
for fn_ in SFHs_fnames])
ts = fits.getdata(SFHs_fnames[0], 'allts')
zs, zmasks = zip(*[masked_z_at_value(WMAP9.age, t_ * u.Gyr) for t_ in ts])
zs = np.ma.array(zs, mask=zmasks)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
SFRDmean = SFHs.mean(axis=0) / WMAP9.scale_factor(zs)**3.
ax.plot(1. / WMAP9.scale_factor(zs), SFRDmean / SFRDmean.max())
ax.set_xlim([1., 1. / WMAP9.scale_factor(10.)])
ax.set_yscale('log')
ax.set_ylim([.009, 1.05])
ax.set_xlabel(r'$\frac{1}{a}$', size='x-small')
ax.set_ylabel(r'$\log{\psi}$', size='x-small')
ax.tick_params(labelsize='x-small', which='both')
ax_ = ax.twiny()
ax_.set_xlim(ax.get_xlim())
zticks = np.linspace(0., 10., 11)
inv_sf_ticks = 1. / WMAP9.scale_factor(zticks)
ax_.set_xticks(inv_sf_ticks, minor=False)
ax_.set_xticklabels(zticks)
ax_.tick_params(labelsize='x-small')
ax_.set_xlabel(r'$z$', size='x-small')
fig.suptitle('``Cosmic" SFR', size='small')
savefig(fig, 'CosmicSFR.png', CSP_dir, close=True)
def make_weightfig(self,
xqty,
yqty,
xbds=[None, None],
ybds=[None, None],
logx=False,
logy=False,
xbins=20,
ybins=20,
overplot_ngroups=0):
xdata = self.trn_metadata[xqty].data
ydata = self.trn_metadata[yqty].data
if logx:
xdata = np.log10(xdata)
if logy:
ydata = np.log10(ydata)
xbds = figures_tools.decide_lims_pctls(xdata, bds=xbds)
ybds = figures_tools.decide_lims_pctls(ydata, bds=ybds)
xbinedges = np.linspace(*xbds, xbins + 1)
ybinedges = np.linspace(*ybds, ybins + 1)
xbinctrs = 0.5 * (xbinedges[:-1] + xbinedges[1:])
ybinctrs = 0.5 * (ybinedges[:-1] + ybinedges[1:])
rawhist, *_ = np.histogram2d(xdata, ydata, bins=[xbinedges, ybinedges])
wthist, *_ = np.histogram2d(xdata,
ydata,
bins=[xbinedges, ybinedges],
weights=self.trn_usage_cts)
Xgrid, Ygrid = np.meshgrid(xbinctrs, ybinctrs, indexing='ij')
fig, ax = plt.subplots(1, 1)
raw_cs = ax.contour(Xgrid,
Ygrid,
rawhist,
colors='C0',
linewidths=0.25)
wtd_cs = ax.contour(Xgrid, Ygrid, wthist, colors='C1', linewidths=0.25)
raw_cs.collections[-1].set_label('all')
wtd_cs.collections[-1].set_label('wtd')
if overplot_ngroups > 0:
self._overplot_models(overplot_ngroups, ax, xdata, ydata)
if (xqty[0] == 'C') and (yqty[0] == 'C'):
self._add_color_arrow(c1=xqty, c2=yqty, ax=ax)
ax.legend()
ax.set_xlabel(xqty.replace('_', '\_'), size='x-small')
ax.set_ylabel(yqty.replace('_', '\_'), size='x-small')
fname = '_'.join(('wtfig', xqty, yqty)) + '.png'
figures_tools.savefig(fig, fname=fname, fdir=self.workdir)
def make_weighthist(self, **kwargs):
'''
histogram of total template usage
'''
fig, ax = plt.subplots(1, 1)
histkws = dict(histtype='step', normed=True)
histkws.update(**kwargs)
bins = np.unique(
np.logspace(0, np.log10(len(self.trn_usage_cts) + 1),
21, dtype=int)) - .5
ax.hist(self.trn_usage_cts + 1, bins=bins, **histkws)
ax.set_xlabel(r'$N + 1$', size='x-small')
ax.set_xscale('log')
ax.set_yscale('log')
fname = 'wthist.png'
figures_tools.savefig(fig, fname=fname, fdir=self.workdir)
def make_weighthist(self, **kwargs):
'''
histogram of total template usage
'''
fig, ax = plt.subplots(1, 1)
histkws = dict(histtype='step', normed=True)
histkws.update(**kwargs)
bins = np.unique(
np.logspace(
0, np.log10(len(self.trn_usage_cts) + 1), 21, dtype=int)) - .5
ax.hist(self.trn_usage_cts + 1, bins=bins, **histkws)
ax.set_xlabel(r'$N + 1$', size='x-small')
ax.set_xscale('log')
ax.set_yscale('log')
fname = 'wthist.png'
figures_tools.savefig(fig, fname=fname, fdir=self.workdir)
def make_weightfig(self, xqty, yqty, xbds=[None, None], ybds=[None, None],
logx=False, logy=False, xbins=20, ybins=20, overplot_ngroups=0):
xdata = self.trn_metadata[xqty].data
ydata = self.trn_metadata[yqty].data
if logx:
xdata = np.log10(xdata)
if logy:
ydata = np.log10(ydata)
xbds = figures_tools.decide_lims_pctls(xdata, bds=xbds)
ybds = figures_tools.decide_lims_pctls(ydata, bds=ybds)
xbinedges = np.linspace(*xbds, xbins + 1)
ybinedges = np.linspace(*ybds, ybins + 1)
xbinctrs = 0.5 * (xbinedges[:-1] + xbinedges[1:])
ybinctrs = 0.5 * (ybinedges[:-1] + ybinedges[1:])
rawhist, *_ = np.histogram2d(xdata, ydata, bins=[xbinedges, ybinedges])
wthist, *_ = np.histogram2d(xdata, ydata, bins=[xbinedges, ybinedges],
weights=self.trn_usage_cts)
Xgrid, Ygrid = np.meshgrid(xbinctrs, ybinctrs, indexing='ij')
fig, ax = plt.subplots(1, 1)
raw_cs = ax.contour(
Xgrid, Ygrid, rawhist, colors='C0', linewidths=0.25)
wtd_cs = ax.contour(
Xgrid, Ygrid, wthist, colors='C1', linewidths=0.25)
raw_cs.collections[-1].set_label('all')
wtd_cs.collections[-1].set_label('wtd')
if overplot_ngroups > 0:
self._overplot_models(overplot_ngroups, ax, xdata, ydata)
if (xqty[0] == 'C') and (yqty[0] == 'C'):
self._add_color_arrow(c1=xqty, c2=yqty, ax=ax)
ax.legend()
ax.set_xlabel(xqty.replace('_', '\_'), size='x-small')
ax.set_ylabel(yqty.replace('_', '\_'), size='x-small')
fname = '_'.join(('wtfig', xqty, yqty)) + '.png'
figures_tools.savefig(fig, fname=fname, fdir=self.workdir)
def make_cosmic_sfr(CSP_dir):
SFHs_fnames = glob.glob(os.path.join(CSP_dir, 'SFHs_*.fits'))
nsubpersfh = fits.getval(SFHs_fnames[0], ext=0, keyword='NSUBPER')
SFHs = np.row_stack([
fits.getdata(fn_, 'allsfhs') /
fits.getdata(fn_, 'mformed')[::nsubpersfh, None] for fn_ in SFHs_fnames
])
ts = fits.getdata(SFHs_fnames[0], 'allts')
zs, zmasks = zip(*[masked_z_at_value(WMAP9.age, t_ * u.Gyr) for t_ in ts])
zs = np.ma.array(zs, mask=zmasks)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
SFRDmean = SFHs.mean(axis=0) / WMAP9.scale_factor(zs)**3.
ax.plot(1. / WMAP9.scale_factor(zs), SFRDmean / SFRDmean.max())
ax.set_xlim([1., 1. / WMAP9.scale_factor(10.)])
ax.set_yscale('log')
ax.set_ylim([.009, 1.05])
ax.set_xlabel(r'$\frac{1}{a}$', size='x-small')
ax.set_ylabel(r'$\log{\psi}$', size='x-small')
ax.tick_params(labelsize='x-small', which='both')
ax_ = ax.twiny()
ax_.set_xlim(ax.get_xlim())
zticks = np.linspace(0., 10., 11)
inv_sf_ticks = 1. / WMAP9.scale_factor(zticks)
ax_.set_xticks(inv_sf_ticks, minor=False)
ax_.set_xticklabels(zticks)
ax_.tick_params(labelsize='x-small')
ax_.set_xlabel(r'$z$', size='x-small')
fig.suptitle('``Cosmic" SFR', size='small')
savefig(fig, 'CosmicSFR.png', CSP_dir, close=True)
def make_color_compare(CSP_dir,
mpl_v,
n_obs=10,
model_zcolors=False,
randomseed=None,
snr_min=5.):
'''
make (g-r) vs (r-i) plot
'''
if randomseed is not None:
np.random.seed(randomseed)
drpall = m.load_drpall(mpl_v, index='plateifu')
drpall = drpall[(drpall['mngtarg2'] == 0) * (drpall['nsa_z'] != -9999)]
plateifus = np.random.choice(drpall['plateifu'], replace=False, size=n_obs)
# cols are g, r, and i fluxes in order
obs_fluxes = [
m.get_gal_bpfluxes(*plateifu.split('-'), mpl_v, ['g', 'r', 'i'],
snr_min) for plateifu in plateifus
]
obs_ext = np.concatenate([
np.repeat(drpall.loc[plateifu]['nsa_extinction'][None, 3:6],
f.shape[0],
axis=0) for f, plateifu in zip(obs_fluxes, plateifus)
])
obs_fluxes = np.row_stack(obs_fluxes)
integrated_g = drpall['nsa_elpetro_absmag'][:, 3] - \
drpall['nsa_extinction'][:, 3]
integrated_r = drpall['nsa_elpetro_absmag'][:, 4] - \
drpall['nsa_extinction'][:, 4]
integrated_i = drpall['nsa_elpetro_absmag'][:, 5] - \
drpall['nsa_extinction'][:, 5]
integrated_gr = integrated_g - integrated_r
integrated_ri = integrated_r - integrated_i
obs_gr = -2.5 * np.log10(obs_fluxes[:, 0] / obs_fluxes[:, 1]) - \
(obs_ext[:, 0] - obs_ext[:, 1])
obs_ri = -2.5 * np.log10(obs_fluxes[:, 1] / obs_fluxes[:, 2]) - \
(obs_ext[:, 1] - obs_ext[:, 2])
models_fnames = glob.glob(os.path.join(CSP_dir, 'CSPs_*.fits'))
if model_zcolors:
color1 = 'Cgr_z015'
color2 = 'Cri_z015'
color1TeX = r'$^{.15}C_{gr}$'
color2TeX = r'$^{.15}C_{ri}$'
else:
color1 = 'Cgr'
color2 = 'Cri'
color1TeX = r'$C_{gr}$'
color2TeX = r'$C_{ri}$'
models_colors = t.vstack(
[t.Table.read(fn_)[color1, color2] for fn_ in models_fnames])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='scatter_density')
ax.scatter([-100], [-100], c='g', s=1, marker='s', label='models')
ax.scatter([-100], [-100], c='b', s=1, marker='s', label='spax')
ax.scatter([-100], [-100], c='r', s=1, marker='s', label='integrated')
ax.scatter_density(obs_gr, obs_ri, color='b')
ax.scatter_density(models_colors[color1], models_colors[color2], color='g')
#ax.scatter_density(integrated_gr, integrated_ri, color='r')
ax.set_xlim([-.2, 1.4])
ax.set_ylim([-.1, 0.6])
ax.set_xlabel(color1TeX, size='small')
ax.set_ylabel(color2TeX, size='small')
ax.legend(loc='best')
fig.suptitle(CSP_dir.replace('_', '-'), size='x-small')
fig.tight_layout()
ax.tick_params(labelsize='x-small')
basename = 'Cgr_Cri'
if model_zcolors:
basename = '_'.join((basename, 'z015'))
savefig(fig, ''.join((basename, '.png')), CSP_dir, close=True)
def savefig(self):
savefig(self.fig, fname='snr_vs_goodpdf.png', fdir='.')
def make_color_compare(CSP_dir, mpl_v, n_obs=10, model_zcolors=False,
randomseed=None, snr_min=5.):
'''
make (g-r) vs (r-i) plot
'''
if randomseed is not None:
np.random.seed(randomseed)
drpall = m.load_drpall(mpl_v, index='plateifu')
drpall = drpall[(drpall['mngtarg2'] == 0) * (drpall['nsa_z'] != -9999)]
plateifus = np.random.choice(drpall['plateifu'], replace=False, size=n_obs)
# cols are g, r, and i fluxes in order
obs_fluxes = [m.get_gal_bpfluxes(*plateifu.split('-'), mpl_v,
['g', 'r', 'i'], snr_min)
for plateifu in plateifus]
obs_ext = np.concatenate(
[np.repeat(drpall.loc[plateifu]['nsa_extinction'][None, 3:6],
f.shape[0], axis=0)
for f, plateifu in zip(obs_fluxes, plateifus)])
obs_fluxes = np.row_stack(obs_fluxes)
integrated_g = drpall['nsa_elpetro_absmag'][:, 3] - \
drpall['nsa_extinction'][:, 3]
integrated_r = drpall['nsa_elpetro_absmag'][:, 4] - \
drpall['nsa_extinction'][:, 4]
integrated_i = drpall['nsa_elpetro_absmag'][:, 5] - \
drpall['nsa_extinction'][:, 5]
integrated_gr = integrated_g - integrated_r
integrated_ri = integrated_r - integrated_i
obs_gr = -2.5 * np.log10(obs_fluxes[:, 0] / obs_fluxes[:, 1]) - \
(obs_ext[:, 0] - obs_ext[:, 1])
obs_ri = -2.5 * np.log10(obs_fluxes[:, 1] / obs_fluxes[:, 2]) - \
(obs_ext[:, 1] - obs_ext[:, 2])
models_fnames = glob.glob(os.path.join(CSP_dir, 'CSPs_*.fits'))
if model_zcolors:
color1 = 'Cgr_z015'
color2 = 'Cri_z015'
color1TeX = r'$^{.15}C_{gr}$'
color2TeX = r'$^{.15}C_{ri}$'
else:
color1 = 'Cgr'
color2 = 'Cri'
color1TeX = r'$C_{gr}$'
color2TeX = r'$C_{ri}$'
models_colors = t.vstack([t.Table.read(fn_)[color1, color2]
for fn_ in models_fnames])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='scatter_density')
ax.scatter([-100], [-100], c='g', s=1, marker='s', label='models')
ax.scatter([-100], [-100], c='b', s=1, marker='s', label='spax')
ax.scatter([-100], [-100], c='r', s=1, marker='s', label='integrated')
ax.scatter_density(obs_gr, obs_ri, color='b')
ax.scatter_density(models_colors[color1], models_colors[color2], color='g')
#ax.scatter_density(integrated_gr, integrated_ri, color='r')
ax.set_xlim([-.2, 1.4])
ax.set_ylim([-.1, 0.6])
ax.set_xlabel(color1TeX, size='small')
ax.set_ylabel(color2TeX, size='small')
ax.legend(loc='best')
fig.suptitle(CSP_dir.replace('_', '-'), size='x-small')
fig.tight_layout()
ax.tick_params(labelsize='x-small')
basename = 'Cgr_Cri'
if model_zcolors:
basename = '_'.join((basename, 'z015'))
savefig(fig, ''.join((basename, '.png')), CSP_dir, close=True)