def test_mileshc():
tpl = TemplateLibrary('MILESHC',
match_resolution=False,
velscale_ratio=4,
spectral_step=1e-4,
log=True,
hardcopy=False)
# Shape
assert tpl.ntpl == 42, 'Incorrect number of templates'
assert tpl['FLUX'].data.shape == (
42, 12634), 'Incorrect shape of the flux array'
# Wavelength and velocity coordinates
assert numpy.isclose(spectral_coordinate_step(tpl['WAVE'].data, log=True), 1e-4/4), \
'Coordinate step is wrong'
assert numpy.isclose(spectrum_velocity_scale(tpl['WAVE'].data),
astropy.constants.c.to('km/s').value*1e-4*numpy.log(10.)/4), \
'Velocity step is wrong'
# Mask
nmasked = {}
for k in tpl.bitmask.keys():
nmasked[k] = numpy.sum(tpl.bitmask.flagged(tpl['MASK'].data, flag=k))
assert numpy.sum(list(nmasked.values())) == 192, 'Masking has changed'
assert nmasked['NO_DATA'] == 0, 'There should be no NO_DATA flags'
# Flux values
flux = numpy.ma.MaskedArray(tpl['FLUX'].data, mask=tpl['MASK'].data > 0)
assert numpy.isclose(numpy.ma.mean(flux),
1.0), 'Templates should be normalized'
def fom_lambda(plt,
ifu,
wave,
flux,
error,
model,
fit='sc',
wave_limits=None,
ofile=None):
if wave_limits is not None:
indx = (wave < wave_limits[0]) | (wave > wave_limits[1])
flux[indx, :] = numpy.ma.masked
error[indx, :] = numpy.ma.masked
model[indx, :] = numpy.ma.masked
else:
indx = numpy.arange(len(wave))[numpy.any(numpy.invert(model.mask),
axis=1)]
wave_limits = [wave[indx[0]], wave[indx[-1]]]
nspec = flux.shape[1]
resid = numpy.ma.absolute(flux - model)
fresid = numpy.ma.absolute(numpy.ma.divide(resid, model))
chi = numpy.ma.absolute(numpy.ma.divide(resid, error))
snr = numpy.ma.divide(flux, error)
mean_flux = numpy.ma.mean(flux, axis=1)
mean_snr = numpy.ma.mean(snr, axis=1)
mean_resid = numpy.ma.mean(resid, axis=1)
mean_fresid = numpy.ma.mean(fresid, axis=1)
mean_chi = numpy.ma.mean(chi, axis=1)
bc = BoxcarFilter(100)
resid_sm = bc.smooth(resid)
fresid_sm = bc.smooth(fresid)
chi_sm = bc.smooth(chi)
spec_lim = [-10, nspec + 10]
flux_lim = numpy.exp(
growth_lim(numpy.ma.log(mean_flux).compressed(), 0.99, fac=2.0))
snr_lim = numpy.exp(
growth_lim(numpy.ma.log(mean_snr).compressed(), 0.99, fac=2.0))
resid_lim = numpy.exp(
growth_lim(numpy.ma.log(mean_resid).compressed(), 0.99, fac=2.0))
fresid_lim = numpy.exp(
growth_lim(numpy.ma.log(mean_fresid).compressed(), 0.99, fac=2.0))
chi_lim = numpy.exp(
growth_lim(numpy.ma.log(mean_chi).compressed(),
0.99,
fac=2.0,
midpoint=0))
l0 = numpy.log10(wave[0])
dl = spectral_coordinate_step(wave, log=True)
wave_bins = numpy.power(10, l0 - dl / 2 + dl * numpy.arange(len(wave) + 1))
spec_bins = 0.5 + numpy.arange(nspec + 1)
font = {'size': 10}
rc('font', **font)
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
ax = init_ax(fig, [0.2, 0.87, 0.60, 0.12], facecolor='0.95', grid=True)
ax.set_xlim(wave_limits)
ax.set_ylim(flux_lim)
ax.set_yscale('log')
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.plot(wave, numpy.ma.mean(flux, axis=1), color='k', lw=0.5)
ax.text(-0.15,
0.5,
r'Flux',
horizontalalignment='center',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
axt = ax.twinx()
axt.set_xlim(wave_limits)
axt.set_ylim(snr_lim)
axt.set_yscale('log')
axt.tick_params(axis='y', colors='0.5')
axt.xaxis.set_major_formatter(ticker.NullFormatter())
axt.plot(wave, numpy.ma.mean(snr, axis=1), color='0.5', lw=0.5)
axt.text(1.1,
0.5,
r'S/N',
horizontalalignment='center',
verticalalignment='center',
rotation='vertical',
transform=axt.transAxes,
color='0.5')
ax = init_ax(fig, [0.2, 0.75, 0.60, 0.12], facecolor='0.95', grid=True)
ax.set_xlim(wave_limits)
ax.set_ylim(resid_lim)
ax.set_yscale('log')
ax.xaxis.set_major_formatter(ticker.NullFormatter())
# ax.plot(wave, numpy.ma.mean(resid_sm, axis=0), color='C3')
ax.plot(wave, numpy.ma.mean(resid, axis=1), color='k', lw=0.5)
ax.text(-0.15,
0.5,
r'$|\Delta|$',
horizontalalignment='center',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
ax = init_ax(fig, [0.2, 0.63, 0.60, 0.12], facecolor='0.95', grid=True)
ax.set_xlim(wave_limits)
ax.set_ylim(fresid_lim)
ax.set_yscale('log')
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.plot(wave, numpy.ma.mean(fresid, axis=1), color='k', lw=0.5)
ax.text(-0.15,
0.5,
r'$|\Delta|/m$',
horizontalalignment='center',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
ax = init_ax(fig, [0.2, 0.51, 0.60, 0.12], facecolor='0.95', grid=True)
ax.set_xlim(wave_limits)
ax.set_ylim(chi_lim)
ax.set_yscale('log')
ax.plot(wave, numpy.ma.mean(chi, axis=1), color='k', lw=0.5)
ax.text(0.5,
-0.41,
r'Wavelength ($\AA$)',
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
ax.text(-0.15,
0.5,
r'$|\Delta|/\epsilon$',
horizontalalignment='center',
verticalalignment='center',
rotation='vertical',
transform=ax.transAxes)
label = '{0}-{1}'.format(plt, ifu)
label += (' STELLAR' if fit == 'sc' else ' EMLINE')
ax.text(1.23,
-0.3,
label,
horizontalalignment='center',
verticalalignment='bottom',
rotation='vertical',
transform=ax.transAxes,
fontsize=22)
ax = init_ax(fig, [0.07, 0.23, 0.30, 0.16], facecolor='0.95')
ax.set_xlim(wave_limits)
ax.set_ylim(spec_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
im = ax.pcolormesh(wave_bins,
spec_bins,
resid.T,
norm=colors.LogNorm(vmin=resid_lim[0],
vmax=resid_lim[1]),
cmap='viridis',
zorder=4,
lw=0,
rasterized=True)
ax = init_ax(fig, [0.07, 0.07, 0.30, 0.16], facecolor='0.95')
ax.set_xlim(wave_limits)
ax.set_ylim(spec_lim)
ax.pcolormesh(wave_bins,
spec_bins,
resid_sm.T,
norm=colors.LogNorm(vmin=resid_lim[0], vmax=resid_lim[1]),
cmap='viridis',
zorder=4,
lw=0,
rasterized=True)
cax = fig.add_axes([0.07, 0.395, 0.20, 0.01])
cb = pyplot.colorbar(
im, cax=cax,
orientation='horizontal') #, format=FormatStrFormatter('%d'))
cb.ax.tick_params(axis='x',
which='both',
bottom=False,
top=True,
labelbottom=False,
labeltop=True)
cax.text(1.05,
0.7,
r'$|\Delta|$',
horizontalalignment='left',
verticalalignment='center',
transform=cax.transAxes,
fontsize=10)
ax = init_ax(fig, [0.37, 0.23, 0.30, 0.16], facecolor='0.95')
ax.set_xlim(wave_limits)
ax.set_ylim(spec_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
im = ax.pcolormesh(wave_bins,
spec_bins,
fresid.T,
norm=colors.LogNorm(vmin=fresid_lim[0],
vmax=fresid_lim[1]),
cmap='viridis',
zorder=4,
lw=0,
rasterized=True)
ax = init_ax(fig, [0.37, 0.07, 0.30, 0.16], facecolor='0.95')
ax.set_xlim(wave_limits)
ax.set_ylim(spec_lim)
ax.yaxis.set_major_formatter(ticker.NullFormatter())
ax.pcolormesh(wave_bins,
spec_bins,
fresid_sm.T,
norm=colors.LogNorm(vmin=fresid_lim[0], vmax=fresid_lim[1]),
cmap='viridis',
zorder=4,
lw=0,
rasterized=True)
ax.text(0.5,
-0.30,
r'Wavelength ($\AA$)',
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
cax = fig.add_axes([0.37, 0.395, 0.20, 0.01])
cb = pyplot.colorbar(
im, cax=cax,
orientation='horizontal') #, format=FormatStrFormatter('%d'))
cb.ax.tick_params(axis='x',
which='both',
bottom=False,
top=True,
labelbottom=False,
labeltop=True)
cax.text(1.05,
0.7,
r'$|\Delta|/m$',
horizontalalignment='left',
verticalalignment='center',
transform=cax.transAxes,
fontsize=10)
ax = init_ax(fig, [0.67, 0.23, 0.30, 0.16], facecolor='0.95')
ax.set_xlim(wave_limits)
ax.set_ylim(spec_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
im = ax.pcolormesh(wave_bins,
spec_bins,
chi.T,
norm=colors.LogNorm(vmin=chi_lim[0], vmax=chi_lim[1]),
cmap='RdBu_r',
zorder=4,
lw=0,
rasterized=True)
ax = init_ax(fig, [0.67, 0.07, 0.30, 0.16], facecolor='0.95')
ax.set_xlim(wave_limits)
ax.set_ylim(spec_lim)
ax.yaxis.set_major_formatter(ticker.NullFormatter())
ax.pcolormesh(wave_bins,
spec_bins,
chi_sm.T,
norm=colors.LogNorm(vmin=chi_lim[0], vmax=chi_lim[1]),
cmap='RdBu_r',
zorder=4,
lw=0,
rasterized=True)
cax = fig.add_axes([0.67, 0.395, 0.20, 0.01])
cb = pyplot.colorbar(
im, cax=cax,
orientation='horizontal') #, format=FormatStrFormatter('%d'))
cb.ax.tick_params(axis='x',
which='both',
bottom=False,
top=True,
labelbottom=False,
labeltop=True)
cax.text(1.05,
0.7,
r'$|\Delta|/\epsilon$',
horizontalalignment='left',
verticalalignment='center',
transform=cax.transAxes,
fontsize=10)
if ofile is None:
pyplot.show()
else:
print('Writing: {0}'.format(ofile))
fig.canvas.print_figure(ofile, bbox_inches='tight', dpi=100)
fig.clear()
pyplot.close(fig)
def main(args):
# Get the g and r filter curves
filter_root = os.path.join(defaults.dap_data_root(), 'filter_response')
f = os.path.join(filter_root, 'gunn_2001_g_response.db')
if not os.path.isfile(f):
raise FileNotFoundError('No file: {0}'.format(f))
db = numpy.genfromtxt(os.path.join(f))
g = interpolate.interp1d(db[:, 0],
db[:, 1],
fill_value=0.0,
bounds_error=False)
f = os.path.join(filter_root, 'gunn_2001_r_response.db')
if not os.path.isfile(f):
raise FileNotFoundError('No file: {0}'.format(f))
db = numpy.genfromtxt(os.path.join(f))
r = interpolate.interp1d(db[:, 0],
db[:, 1],
fill_value=0.0,
bounds_error=False)
# Build the template library
tpl = TemplateLibrary(args.key,
velscale_ratio=args.velscale_ratio,
spectral_step=args.step,
log=not args.linear,
hardcopy=False)
# Get the sampling
nwave = len(tpl['WAVE'].data)
dwave = spectral_coordinate_step(tpl['WAVE'].data, log=not args.linear)
if not args.linear:
dwave *= tpl['WAVE'].data * numpy.log(10.)
flxmid = (numpy.amax(tpl['FLUX'].data, axis=1) +
numpy.amin(tpl['FLUX'].data, axis=1)) / 2.0
flxrng = numpy.amax(tpl['FLUX'].data, axis=1) - numpy.amin(
tpl['FLUX'].data, axis=1)
g_tc = g(tpl['WAVE'].data)
r_tc = r(tpl['WAVE'].data)
g_tc_sum = numpy.sum(g_tc * dwave)
r_tc_sum = numpy.sum(r_tc * dwave)
gflux = numpy.sum(tpl['FLUX'].data * g_tc[None, :] * dwave[None, :],
axis=1) / g_tc_sum
rflux = numpy.sum(tpl['FLUX'].data * r_tc[None, :] * dwave[None, :],
axis=1) / r_tc_sum
numpy.savetxt(
args.ofile,
numpy.array([
numpy.arange(tpl.ntpl), flxmid, flxrng, gflux, rflux,
-2.5 * numpy.log10(gflux / rflux)
]).T,
fmt=['%5d', '%15.8e', '%15.8e', '%15.8e', '%15.8e', '%15.8e'],
header='{0:>3} {1:>15} {2:>15} {3:>15} {4:>15} {5:>15}'.format(
'ID', 'FLXMID', 'FLXRNG', 'MEANg', 'MEANr', '-2.5LOG(g/r)'))