def fom_maps(plt,
ifu,
image_file,
snr,
color,
rms,
frms,
rchi2,
chi_grw,
extent=None,
fit='sc',
ofile=None):
color_lim = numpy.exp(growth_lim(numpy.ma.log(color), 0.95, fac=1.1)) \
if fit != 'sc' else growth_lim(color, 0.95, fac=1.10)
snr_lim = numpy.power(10., growth_lim(numpy.ma.log10(snr), 0.90, fac=1.05))
rms_lim = numpy.power(10., growth_lim(numpy.ma.log10(rms), 0.90, fac=1.05))
frm_lim = numpy.power(10., growth_lim(numpy.ma.log10(frms), 0.90,
fac=1.05))
chi_lim = [1 / 4, 4]
c68_lim = [1 / 4, 4]
c99_lim = [2.6 / 4, 2.6 * 4]
crt_lim = [0.8, 2]
left = 0.035
bott = 0.08
imwd = 0.24
hbuf = 0.08
cbuf = 0.005
cbwd = 0.01
vbuf = 0.03
font = {'size': 6}
rc('font', **font)
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
i, j = 0, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
if os.path.isfile(image_file):
img = image.imread(image_file)
ax.imshow(img)
ax.text(0.95,
0.95,
r'SDSS',
color='white',
ha='right',
va='center',
transform=ax.transAxes)
else:
ax.text(0.5,
0.5,
'No Image',
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=20)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
i, j = 1, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
snr,
extent=extent,
norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]),
cmap='viridis',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'S/N$_g$' if fit == 'sc' else r'H$\alpha$ A/N',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
label = '{0}-{1}'.format(plt, ifu)
label += (' STELLAR' if fit == 'sc' else ' EMLINE')
ax.text(0.5,
1.1,
label,
horizontalalignment='center',
verticalalignment='bottom',
transform=ax.transAxes,
fontsize=26)
i, j = 2, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
kwargs = {}
if fit == 'sc':
# Plotting the g-r color
kwargs['vmin'] = color_lim[0]
kwargs['vmax'] = color_lim[1]
else:
# Plotting H-alpha/H-beta flux ratio
kwargs['norm'] = colors.LogNorm(vmin=color_lim[0], vmax=color_lim[1])
masked_imshow(fig,
ax,
cax,
color,
extent=extent,
cmap='RdBu_r',
zorder=3,
**kwargs)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'$g-r$' if fit == 'sc' else r'H$\alpha$/H$\beta$',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
i, j = 0, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
rms,
extent=extent,
norm=colors.LogNorm(vmin=rms_lim[0], vmax=rms_lim[1]),
cmap='viridis',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'RMS',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
i, j = 1, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
frms,
extent=extent,
norm=colors.LogNorm(vmin=frm_lim[0], vmax=frm_lim[1]),
cmap='viridis',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'fRMS',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
i, j = 2, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
rchi2,
extent=extent,
norm=colors.LogNorm(vmin=chi_lim[0], vmax=chi_lim[1]),
cmap='RdBu_r',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'$\chi^2$',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
i, j = 0, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
chi_grw[0],
extent=extent,
norm=colors.LogNorm(vmin=c68_lim[0], vmax=c68_lim[1]),
cmap='viridis',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'68% Growth $|\Delta|/\epsilon$',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
i, j = 1, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
chi_grw[1],
extent=extent,
norm=colors.LogNorm(vmin=c99_lim[0], vmax=c99_lim[1]),
cmap='viridis',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'99% Growth $|\Delta|/\epsilon$',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
x = numpy.linspace(0, 5, 100)
g = (erf(x / numpy.sqrt(2)) - erf(-x / numpy.sqrt(2))) / 2.
interp = interpolate.interp1d(g, x)
m = interp([0.68, 0.99])
i, j = 2, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
masked_imshow(fig,
ax,
cax,
numpy.ma.divide(chi_grw[1], chi_grw[0]) * m[0] / m[1],
extent=extent,
vmin=crt_lim[0],
vmax=crt_lim[1],
cmap='RdBu_r',
zorder=3)
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.95,
0.95,
r'99%/68% wrt Gaussian',
ha='right',
va='center',
transform=ax.transAxes,
zorder=4)
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 radial_coverage_histogram(dapall, daptype, ofile=None):
# Must have finished, be of the correct daptype, and *not* be
# critical
indx = (dapall['DAPDONE'] == 1) & (dapall['DAPTYPE'] == daptype) \
& numpy.invert(DAPQualityBitMask().flagged(dapall['DAPQUAL'], 'CRITICAL'))
# Get the range
arcsec_rng = growth_lim(dapall['RCOV90'][indx], 0.99, fac=1.1)
rore = numpy.ma.divide(dapall['RCOV90'][indx],
dapall['NSA_ELPETRO_TH50_R'][indx])
rore[dapall['NSA_ELPETRO_TH50_R'][indx] < 0] = numpy.ma.masked
rore_rng = growth_lim(rore, 0.99, fac=1.1)
# Determine the sample
sdssMaskbits = defaults.sdss_maskbits_file()
targ1bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
ancillary = dapall['MNGTARG3'][indx] > 0
primaryplus = targ1bm.flagged(
dapall['MNGTARG1'][indx],
flag=['PRIMARY_v1_2_0', 'COLOR_ENHANCED_v1_2_0'])
secondary = targ1bm.flagged(dapall['MNGTARG1'][indx],
flag='SECONDARY_v1_2_0')
other = numpy.invert(ancillary) & numpy.invert(primaryplus) & numpy.invert(
secondary)
print('Num. of observations with 90% coverage <1 arcsec: {0}'.format(
numpy.sum(dapall['RCOV90'][indx] < 1)))
font = {'size': 10}
rc('font', **font)
# Instantiate the figure
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
nlim = [0.8, 1000]
# Show RCOV90 in terms of arcseconds for each IFU
ax = init_ax(fig, [0.1, 0.58, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonpositive='clip')
ifu = [19, 37, 61, 91, 127]
for i, f in enumerate(ifu):
_indx = indx & ((dapall['IFUDESIGN'] / 100).astype(int) == f)
if not numpy.any(_indx):
continue
ax.hist(dapall['RCOV90'][_indx],
bins=50,
range=arcsec_rng,
alpha=0.5,
histtype='stepfilled',
color='C{0}'.format(i),
zorder=2)
ax.hist(dapall['RCOV90'][_indx],
bins=50,
range=arcsec_rng,
histtype='step',
color='C{0}'.format(i),
zorder=3)
medp = numpy.median(dapall['RCOV90'][_indx])
ax.plot([medp, medp],
nlim,
color='C{0}'.format(i),
zorder=4,
linestyle='--')
ax.text(-0.1,
0.5,
'N',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax.text(0.5,
-0.13,
r'$R_{90}$ (arcsec)',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.95,
'19-fiber',
color='C0',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.90,
'37-fiber',
color='C1',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.85,
'61-fiber',
color='C2',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.80,
'91-fiber',
color='C3',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.75,
'127-fiber',
color='C4',
ha='left',
va='center',
transform=ax.transAxes)
# Show RCOV90 in terms of Re for each sample
ax = init_ax(fig, [0.1, 0.08, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonpositive='clip')
if numpy.any(primaryplus):
_rore = (rore[primaryplus]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C0',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C0',
zorder=3)
medp = numpy.median(_rore)
ax.plot([medp, medp], nlim, color='C0', zorder=4, linestyle='--')
if numpy.any(secondary):
_rore = (rore[secondary]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C1',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C1',
zorder=3)
medp = numpy.median(_rore)
ax.plot([medp, medp], nlim, color='C1', zorder=4, linestyle='--')
if numpy.any(other):
_rore = (rore[other]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C2',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C2',
zorder=3)
if numpy.any(ancillary):
_rore = (rore[ancillary]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C3',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C3',
zorder=3)
ax.text(-0.1,
0.5,
'N',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax.text(0.5,
-0.12,
r'$R_{90}/R_{eff}$',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.95,
'Primary+',
color='C0',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.90,
'Secondary',
color='C1',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.85,
'Other',
color='C2',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.80,
'Ancillary',
color='C3',
ha='left',
va='center',
transform=ax.transAxes)
if ofile is None:
pyplot.show()
else:
print('Writing: {0}'.format(ofile))
fig.canvas.print_figure(ofile, bbox_inches='tight')
fig.clear()
pyplot.close(fig)
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 fom_growth(plt,
ifu,
flux,
error,
model,
snrg,
rms,
frms,
rchi2,
fit='sc',
ofile=None):
nspec = flux.shape[1]
resid = numpy.ma.absolute(flux - model)
fresid = numpy.ma.divide(resid, model)
chi = numpy.ma.divide(resid, error)
snr_lim = numpy.exp(
growth_lim(numpy.ma.log(snrg).compressed(), 0.95, fac=1.3))
chi_lim = numpy.exp(
growth_lim(numpy.ma.log(chi).compressed(), 0.8, fac=1.1))
chi_lim[1] = numpy.ma.amax(chi) * 2
resid_lim = numpy.exp(
growth_lim(numpy.ma.log(resid).compressed(), 0.9, fac=1.1))
resid_lim[1] = numpy.ma.amax(resid) * 2
fresid_lim = numpy.exp(
growth_lim(numpy.ma.log(fresid).compressed(), 0.9, fac=1.1))
fresid_lim[1] = numpy.ma.amax(fresid) * 2
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
rax = init_ax(fig, [0.07, 0.50, 0.30, 0.35], facecolor='0.9', grid=True)
rax.set_xlim(resid_lim)
rax.set_ylim([1e-4, 2])
rax.set_yscale('log')
rax.set_xscale('log')
rax.text(0.5,
-0.17,
r'$|\Delta|$',
ha='center',
va='center',
transform=rax.transAxes)
rax.text(0.1,
0.5,
r'1-Growth',
horizontalalignment='center',
verticalalignment='center',
transform=rax.transAxes,
rotation='vertical')
fax = init_ax(fig, [0.37, 0.50, 0.30, 0.35], facecolor='0.9', grid=True)
fax.set_xlim(fresid_lim)
fax.set_ylim([1e-4, 2])
fax.set_yscale('log')
fax.set_xscale('log')
fax.yaxis.set_major_formatter(ticker.NullFormatter())
label = '{0}-{1}'.format(plt, ifu)
label += (' STELLAR' if fit == 'sc' else ' EMLINE')
fax.text(0.5,
1.3,
label,
horizontalalignment='center',
verticalalignment='center',
transform=fax.transAxes,
fontsize=22)
fax.text(0.5,
-0.17,
r'$|\Delta|/m$',
ha='center',
va='center',
transform=fax.transAxes)
cax = init_ax(fig, [0.67, 0.50, 0.30, 0.35], facecolor='0.9', grid=True)
cax.set_xlim(chi_lim)
cax.set_ylim([1e-4, 2])
cax.set_yscale('log')
cax.set_xscale('log')
cax.yaxis.set_major_formatter(ticker.NullFormatter())
cax.text(0.5,
-0.17,
r'$|\Delta|/\epsilon$',
ha='center',
va='center',
transform=cax.transAxes)
cmap = cm.get_cmap('viridis')
cnorm = colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1])
cbax = fig.add_axes([0.69, 0.86, 0.2, 0.01])
cb = colorbar.ColorbarBase(cbax,
norm=cnorm,
cmap=cmap,
orientation='horizontal')
cb.ax.tick_params(axis='x',
which='both',
bottom=False,
top=True,
labelbottom=False,
labeltop=True)
cbax.text(1.05,
0.5,
r'S/N$_g$',
ha='left',
va='center',
transform=cbax.transAxes)
snr_srt = numpy.argsort(snrg)
for i in range(nspec):
j = snr_srt[i]
if numpy.all(chi.mask[:, j]):
continue
t = resid[:, j].compressed()
s = numpy.argsort(t)
rax.step(t[s],
1 - numpy.arange(len(t)).astype(float) / len(t),
where='pre',
color=cmap(cnorm(snrg[j])),
lw=0.7)
t = fresid[:, j].compressed()
s = numpy.argsort(t)
fax.step(t[s],
1 - numpy.arange(len(t)).astype(float) / len(t),
where='pre',
color=cmap(cnorm(snrg[j])),
lw=0.7)
t = chi[:, j].compressed()
s = numpy.argsort(t)
cax.step(t[s],
1 - numpy.arange(len(t)).astype(float) / len(t),
where='pre',
color=cmap(cnorm(snrg[j])),
lw=0.7)
x = numpy.linspace(0, 5, 100)
g = (erf(x / numpy.sqrt(2)) - erf(-x / numpy.sqrt(2))) / 2.
interp = interpolate.interp1d(g, x)
cax.plot(x, 1 - g, color='k')
chi2_lim = numpy.exp(
growth_lim(numpy.ma.log(rchi2).compressed(), 1.0, fac=2.0))
rms_lim = numpy.exp(
growth_lim(numpy.ma.log(rms).compressed(), 1.0, fac=2.0))
frms_lim = numpy.exp(
growth_lim(numpy.ma.log(frms).compressed(), 1.0, fac=2.0))
snr_lim = numpy.exp(
growth_lim(numpy.ma.log(snrg).compressed(), 1.0, fac=2.0))
rax = init_ax(fig, [0.07, 0.15, 0.30, 0.25], facecolor='0.9', grid=True)
rax.set_xlim(rms_lim)
rax.set_ylim(snr_lim)
rax.set_yscale('log')
rax.set_xscale('log')
rax.scatter(rms, snrg, marker='.', s=20, color='k', lw=0, alpha=0.5)
rax.text(0.5,
-0.25,
r'$RMS$',
ha='center',
va='center',
transform=rax.transAxes)
rax.text(0.1,
0.5,
r'S/N$_g$',
horizontalalignment='center',
verticalalignment='center',
transform=rax.transAxes,
rotation='vertical')
fax = init_ax(fig, [0.37, 0.15, 0.30, 0.25], facecolor='0.9', grid=True)
fax.set_xlim(frms_lim)
fax.set_ylim(snr_lim)
fax.set_yscale('log')
fax.set_xscale('log')
fax.yaxis.set_major_formatter(ticker.NullFormatter())
fax.scatter(frms, snrg, marker='.', s=20, color='k', lw=0, alpha=0.5)
fax.text(0.5,
-0.25,
r'$fRMS$',
ha='center',
va='center',
transform=fax.transAxes)
cax = init_ax(fig, [0.67, 0.15, 0.30, 0.25], facecolor='0.9', grid=True)
cax.set_xlim(chi2_lim)
cax.set_ylim(snr_lim)
cax.set_yscale('log')
cax.set_xscale('log')
cax.yaxis.set_major_formatter(ticker.NullFormatter())
cax.scatter(rchi2, snrg, marker='.', s=20, color='k', lw=0, alpha=0.5)
cax.text(0.5,
-0.25,
r'$\chi^2_\nu$',
ha='center',
va='center',
transform=cax.transAxes)
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 spotcheck_images(cube, plan, method_dir, ref_dir, qa_dir, fwhm=None):
"""
Construct a QA plot for the PPXFFit results.
Args:
cube (:class:`~mangadap.datacube.datacube.DataCube`):
Analyzed data cube
plan (:obj:`dict`, :class:`~mangadap.par.parset.ParSet`):
Object with analysis plan keywords.
method_dir (`Path`_):
Top-level "method" directory with all the DAP files.
ref_dir (`Path`_):
Directory with all the DAP reference files.
qa_dir (`Path`_):
Directory for the QA figures
fwhm (:obj:`float`, optional):
The FWHM in arcsec of the beam. If None, no beam included in the
plot.
"""
# Check that the paths exists
if not method_dir.exists():
raise NotADirectoryError(
f'{method_dir} does not exist; run the DAP first!')
if not ref_dir.exists():
raise NotADirectoryError(
f'{ref_dir} does not exist; run the DAP first!')
if not qa_dir.exists():
qa_dir.mkdir(parents=True)
# Get the name of the output file
ofile = qa_dir / f'{cube.output_root}-MAPS-{plan["key"]}-spotcheck.png'
bm = dapfits.DAPMapsBitMask()
_, directory, file = dapfits.default_paths(cube,
method=plan['key'],
output_path=method_dir)
ifile = directory / file
if not ifile.exists():
raise FileNotFoundError(f'No file: {ifile}')
print(f'Reading: {ifile}')
hdu = fits.open(str(ifile))
# Check everything is finite
num_ext = len(hdu)
for i in range(num_ext):
if hdu[i].data is None:
continue
if not numpy.all(numpy.isfinite(hdu[i].data)):
raise ValueError(f'HDU {hdu[i].name} contains infs or NaNs!')
# Build the column dictionaries
emline = channel_dictionary(hdu, 'EMLINE_GFLUX')
specindex = channel_dictionary(hdu, 'SPECINDEX')
extent = map_extent(hdu, 'SPX_MFLUX')
# Get the data to plot
spxflx = numpy.ma.MaskedArray(hdu['SPX_MFLUX'].data.copy())
spxsnr = numpy.ma.MaskedArray(hdu['SPX_SNR'].data.copy())
binid = numpy.ma.MaskedArray(hdu['BINID'].data.copy(),
mask=hdu['BINID'].data < 0)
binsnr = numpy.ma.MaskedArray(hdu['BIN_SNR'].data.copy(),
mask=hdu['BIN_MFLUX_MASK'].data > 0)
# 68% growth of the absolute value of the fractional residuals
scfres = numpy.ma.MaskedArray(
hdu['STELLAR_FOM'].data.copy()[3, :, :],
mask=numpy.invert(hdu['STELLAR_FOM'].data[3, :, :] > 0))
# Reduced chi-square
scrchi = numpy.ma.MaskedArray(
hdu['STELLAR_FOM'].data.copy()[2, :, :],
mask=numpy.invert(hdu['STELLAR_FOM'].data[2, :, :] > 0))
strvel = numpy.ma.MaskedArray(hdu['STELLAR_VEL'].data.copy(),
mask=bm.flagged(hdu['STELLAR_VEL_MASK'].data,
'DONOTUSE'))
ustrvel = numpy.ma.MaskedArray(hdu['STELLAR_VEL'].data.copy(),
mask=numpy.invert(
bm.flagged(hdu['STELLAR_VEL_MASK'].data,
'UNRELIABLE')))
ustrvel[numpy.invert(numpy.ma.getmaskarray(ustrvel))] = 0.0
strsig = numpy.ma.MaskedArray(hdu['STELLAR_SIGMA'].data.copy(),
mask=bm.flagged(
hdu['STELLAR_SIGMA_MASK'].data,
'DONOTUSE'))
ustrsig = numpy.ma.MaskedArray(hdu['STELLAR_SIGMA'].data.copy(),
mask=numpy.invert(
bm.flagged(
hdu['STELLAR_SIGMA_MASK'].data,
'UNRELIABLE')))
ustrsig[numpy.invert(numpy.ma.getmaskarray(ustrsig))] = 0.0
hasflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhasflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhasflx[numpy.invert(numpy.ma.getmaskarray(uhasflx))] = 0.0
hagflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhagflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhagflx[numpy.invert(numpy.ma.getmaskarray(uhagflx))] = 0.0
hagvel = numpy.ma.MaskedArray(
hdu['EMLINE_GVEL'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(hdu['EMLINE_GVEL_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhagvel = numpy.ma.MaskedArray(
hdu['EMLINE_GVEL'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GVEL_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhagvel[numpy.invert(numpy.ma.getmaskarray(uhagvel))] = 0.0
hagsig = numpy.ma.MaskedArray(
hdu['EMLINE_GSIGMA'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(
hdu['EMLINE_GSIGMA_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhagsig = numpy.ma.MaskedArray(
hdu['EMLINE_GSIGMA'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GSIGMA_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhagsig[numpy.invert(numpy.ma.getmaskarray(uhagsig))] = 0.0
hbsflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Hb-4862'], :, :],
'DONOTUSE'))
uhbsflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Hb-4862'], :, :],
'UNRELIABLE')))
uhbsflx[numpy.invert(numpy.ma.getmaskarray(uhbsflx))] = 0.0
hbgflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Hb-4862'], :, :],
'DONOTUSE'))
uhbgflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Hb-4862'], :, :],
'UNRELIABLE')))
uhbgflx[numpy.invert(numpy.ma.getmaskarray(uhbgflx))] = 0.0
specindex_available = True
try:
if hdu['SPECINDEX'].data is None:
pass
except:
warnings.warn('No spectral indices in maps file.')
specindex_available = False
if specindex_available:
d4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['D4000'], :, :],
mask=bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['D4000'], :, :],
'DONOTUSE'))
ud4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['D4000'], :, :],
mask=numpy.invert(
bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['D4000'], :, :],
'UNRELIABLE')))
ud4000[numpy.invert(numpy.ma.getmaskarray(ud4000))] = 0.0
dn4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['Dn4000'], :, :],
mask=bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['Dn4000'], :, :],
'DONOTUSE'))
udn4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['Dn4000'], :, :],
mask=numpy.invert(
bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['Dn4000'], :, :],
'UNRELIABLE')))
udn4000[numpy.invert(numpy.ma.getmaskarray(udn4000))] = 0.0
else:
output_shape = spxflx.shape
d4000 = numpy.ma.masked_all(output_shape)
ud4000 = numpy.ma.masked_all(output_shape)
dn4000 = numpy.ma.masked_all(output_shape)
udn4000 = numpy.ma.masked_all(output_shape)
# Get the limits to apply
x_order = 1 if extent[0] < extent[1] else -1
Dx = x_order * (extent[1] - extent[0])
y_order = 1 if extent[2] < extent[3] else -1
Dy = y_order * (extent[3] - extent[2])
# Force the image panels to be square; assumes extent has the same units in
# both dimensions.
D = max(Dx, Dy)
x_lim = (extent[0] + extent[1]) / 2 + D * x_order * numpy.array([-1, 1
]) / 2
y_lim = (extent[2] + extent[3]) / 2 + D * y_order * numpy.array([-1, 1
]) / 2
flux_lim = numpy.power(10,
growth_lim(numpy.ma.log10(spxflx), 0.90, fac=1.05))
t = numpy.ma.append(numpy.ma.log10(spxsnr), numpy.ma.log10(binsnr))
snr_lim = numpy.power(10, growth_lim(t, 0.90, fac=1.05))
bin_lim = [numpy.ma.amin(binid), numpy.ma.amax(binid)]
res_lim = numpy.power(10, growth_lim(numpy.ma.log10(scfres),
0.90,
fac=1.05))
chi_lim = growth_lim(scrchi, 0.90, fac=1.05)
t = numpy.ma.append(strvel, hagvel)
vel_lim = growth_lim(t, 0.85, fac=1.10, midpoint=0.0)
if vel_lim[0] < -350:
vel_lim[0] = -350
if vel_lim[1] > 350:
vel_lim[1] = 350
t = numpy.ma.append(numpy.ma.log10(strsig), numpy.ma.log10(hagsig))
sig_lim = numpy.power(10, growth_lim(t, 0.90, fac=1.05))
t = numpy.ma.append(
numpy.ma.append(numpy.ma.log10(hasflx), numpy.ma.log10(hagflx)),
numpy.ma.append(numpy.ma.log10(hbsflx), numpy.ma.log10(hbgflx)))
# t = numpy.ma.log10(t)
hflx_lim = numpy.power(10, growth_lim(t, 0.95, fac=1.10))
# hflx_lim = [ -0.3, 2.2 ]
t = numpy.ma.append(d4000, dn4000)
# d4000_lim = growth_lim(t, 0.90, 1.05)
d4000_lim = [1.0, 2.5]
font = {'size': 6}
rc('font', **font)
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
dx = 0.22
left = 0.05
top = 0.94
dw = 0.005
snr_levels = [0.5, 1.0, 1.5, 2.0]
ax = init_ax(fig, [left, top - dx, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([left + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [flux_lim[0] * 1.1, flux_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
spxflx,
extent=extent,
norm=colors.LogNorm(vmin=flux_lim[0], vmax=flux_lim[1]),
cmap='YlGnBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter())
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'g-band S (spx)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - dx, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + dx + dw + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [snr_lim[0] * 1.1, snr_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
spxsnr,
extent=extent,
norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]),
cmap='YlGnBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'g-band S/N (spx)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
# ax.text(1.0, 1.1, r'{0}-{1}; {2}'.format(plate,ifudesign, hdu['PRIMARY'].header['MANGAID']),
# horizontalalignment='center', verticalalignment='center', transform=ax.transAxes,
# fontsize=20)
ax.text(1.0,
1.1,
f'{cube.name}; {plan["key"]}',
ha='center',
va='center',
transform=ax.transAxes,
fontsize=20)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - dx, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + 2 * dx + 2 * dw + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
masked_imshow(fig,
ax,
cax,
binid[0, :, :],
extent=extent,
vmin=bin_lim[0],
vmax=bin_lim[1],
cmap='CMRmap',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbformat=ticker.ScalarFormatter(),
cbticks=bin_lim)
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'bin ID',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - dx, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + 3 * dx + 3 * dw + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [snr_lim[0] * 1.1, snr_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
binsnr,
extent=extent,
norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]),
cmap='YlGnBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'g-band S/N (bin)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left, top - 2 * dx - dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + dx * 0.65, top - dx - dw - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [res_lim[0] * 1.1, res_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
scfres,
extent=extent,
norm=colors.LogNorm(vmin=res_lim[0], vmax=res_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'pPXF Frac. Resid.',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - 2 * dx - dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + dx + dw + dx * 0.65, top - dx - dw - 0.07 * dx, 0.27 * dx, 0.01
])
cbticks = [
numpy.round(chi_lim[0] + 0.01, decimals=2),
numpy.round(chi_lim[1] - 0.01, decimals=2)
]
masked_imshow(fig,
ax,
cax,
scrchi,
extent=extent,
vmin=chi_lim[0],
vmax=chi_lim[1],
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'${\rm pPXF}\ \chi^2_\nu$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - 2 * dx - dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 2 * dx + 2 * dw + dx * 0.65, top - dx - dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [vel_lim[0] / 1.1, 0.0, vel_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
strvel,
extent=extent,
vmin=vel_lim[0],
vmax=vel_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'$V_\ast$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - 2 * dx - dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 3 * dx + 3 * dw + dx * 0.65, top - dx - dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [
sig_lim[0] * 1.1,
numpy.power(10, numpy.mean(numpy.log10(sig_lim))), sig_lim[1] / 1.1
]
masked_imshow(fig,
ax,
cax,
strsig,
extent=extent,
norm=colors.LogNorm(vmin=sig_lim[0], vmax=sig_lim[1]),
cmap='viridis',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'$\sigma_\ast$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left, top - 3 * dx - 2 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hasflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\alpha\ {\rm flux}$ (sum)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - 3 * dx - 2 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + dx + dw + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hagflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\alpha\ {\rm flux}$ (Gauss)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - 3 * dx - 2 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 2 * dx + 2 * dw + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [vel_lim[0] / 1.1, 0.0, vel_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hagvel,
extent=extent,
vmin=vel_lim[0],
vmax=vel_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'$V_{{\rm H}\alpha}$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - 3 * dx - 2 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 3 * dx + 3 * dw + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [
sig_lim[0] * 1.1,
numpy.power(10, numpy.mean(numpy.log10(sig_lim))), sig_lim[1] / 1.1
]
masked_imshow(fig,
ax,
cax,
hagsig,
extent=extent,
norm=colors.LogNorm(vmin=sig_lim[0], vmax=sig_lim[1]),
cmap='viridis',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'$\sigma_{{\rm H}\alpha}$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left, top - 4 * dx - 3 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
cax = fig.add_axes(
[left + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hbsflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\beta\ {\rm flux}$ (sum)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - 4 * dx - 3 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + dx + dw + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hbgflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\beta\ {\rm flux}$ (Gauss)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - 4 * dx - 3 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 2 * dx + 2 * dw + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
masked_imshow(fig,
ax,
cax,
d4000,
extent=extent,
vmin=d4000_lim[0],
vmax=d4000_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=d4000_lim,
cbformat=ticker.ScalarFormatter())
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'D4000',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - 4 * dx - 3 * dw, dx, dx])
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 3 * dx + 3 * dw + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
masked_imshow(fig,
ax,
cax,
dn4000,
extent=extent,
vmin=d4000_lim[0],
vmax=d4000_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=d4000_lim,
cbformat=ticker.ScalarFormatter())
if fwhm is not None:
ax.add_patch(
map_beam_patch(extent,
ax,
fwhm=fwhm,
facecolor='0.7',
edgecolor='k',
zorder=4))
ax.text(0.99,
0.05,
r'Dn4000',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
if ofile is not None:
fig.canvas.print_figure(ofile, bbox_inches='tight')
print('Writing: {0}'.format(ofile))
else:
pyplot.show()
fig.clear()
pyplot.close(fig)
def stellar_continuum_scatter(plt,
ifu,
daptype,
binid,
snr,
r68,
r99,
rchi2,
signal,
a,
da,
an,
dan,
gmr,
t,
dt,
tn,
dtn,
svel,
ssigo,
ssigcor,
ssigc,
extent=None,
ofile=None):
snr_lim = numpy.power(10., growth_lim(numpy.ma.log10(snr), 1.00, fac=1.05))
snr_lim[0] = max(0.1, snr_lim[0])
r68_lim = numpy.power(10., growth_lim(numpy.ma.log10(r68), 0.90, fac=1.05))
r99_lim = numpy.power(10., growth_lim(numpy.ma.log10(r99), 0.90, fac=1.05))
chi_lim = growth_lim(rchi2, 0.90, fac=1.05)
s_lim = numpy.power(10., growth_lim(numpy.ma.log10(signal), 1.00,
fac=1.10))
a_lim = growth_lim(numpy.ma.log10(a), 0.99, fac=1.10)
da_lim = growth_lim(numpy.ma.log10(da), 0.99, fac=1.10)
an_lim = growth_lim(numpy.ma.log10(an), 1.00, fac=1.10)
dan_lim = growth_lim(numpy.ma.log10(dan), 1.00, fac=1.10)
an_lim = numpy.power(10, an_lim)
dan_lim = numpy.power(10, dan_lim)
gmr_lim = None if gmr is None else growth_lim(gmr, 0.95, fac=1.10)
t_lim = growth_lim(numpy.ma.log10(t), 0.99, fac=1.10)
dt_lim = growth_lim(numpy.ma.log10(dt), 0.99, fac=1.10)
tn_lim = growth_lim(numpy.ma.log10(tn), 1.00, fac=1.10)
dtn_lim = growth_lim(numpy.ma.log10(dtn), 1.00, fac=1.10)
dtn_lim = numpy.power(10, dtn_lim)
tn_lim = numpy.power(10, tn_lim)
sv_lim = growth_lim(svel, 0.90, fac=1.2, midpoint=0)
so_lim = numpy.power(10., growth_lim(numpy.ma.log10(ssigo), 1.00,
fac=1.05))
sc_lim = growth_lim(ssigcor, 0.90, fac=1.05)
ss_lim = numpy.power(10., growth_lim(numpy.ma.log10(ssigc), 0.90,
fac=1.05))
# so_lim = [ min(so_lim[0],ss_lim[0]), max(so_lim[1],ss_lim[1]) ]
# ss_lim = so_lim.copy()
uniq, indx = numpy.unique(binid, return_index=True)
if uniq[0] == -1:
indx = indx[1:]
# fig = pyplot.figure()
# ax = fig.add_subplot(111, projection='3d')
#
# ax.scatter(numpy.ma.log10(dtn[indx]), numpy.ma.log10(dan[indx]), numpy.ma.log10(ssigo[indx]),
# c=snr[indx], cmap='viridis', norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]))
#
# ax.set_xlim(numpy.log10(dtn_lim))
# ax.set_ylim(numpy.log10(dan_lim))
# ax.set_zlim(numpy.log10(so_lim))
# sc = pyplot.scatter(dan[indx], ssigo[indx], marker='.', s=20, c=rchi2[indx],
# cmap='viridis', vmin=chi_lim[0], vmax=chi_lim[1])
# sc = pyplot.scatter(snr[indx], ssigo[indx], marker='.', s=20, c=dan[indx],
# cmap='viridis', norm=colors.LogNorm(vmin=numpy.amin(dan[indx]), vmax=numpy.amax(dan[indx])))
# sc = pyplot.scatter(signal[indx], dan[indx], marker='.', s=20, c=snr[indx],
# cmap='viridis', norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]))
sc = pyplot.scatter(snr[indx],
dan[indx],
marker='.',
s=20,
c=ssigo[indx],
cmap='viridis',
norm=colors.LogNorm(vmin=so_lim[0], vmax=so_lim[1]))
# sc = pyplot.scatter(dan[indx], ssigo[indx], marker='.', s=20, c=snr[indx],
# cmap='viridis', norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]))
# sc = pyplot.scatter(dtn[indx], dan[indx], marker='.', s=20, c=snr[indx],
# cmap='viridis', norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]))
cb = pyplot.colorbar(sc)
## pyplot.xlim(s_lim)
pyplot.ylim(dan_lim)
pyplot.xlim(snr_lim)
pyplot.xscale('log')
pyplot.yscale('log')
pyplot.show()
exit()
def stellar_continuum_maps(plt,
ifu,
daptype,
snr,
r68,
r99,
rchi2,
signal,
a,
da,
an,
dan,
gmr,
t,
dt,
tn,
dtn,
svel,
ssigo,
ssigcor,
ssigc,
extent=None,
ofile=None):
font = {'size': 6}
rc('font', **font)
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(2 * w, 2 * h))
# Dx = (extent[0]-extent[1])*1.1
# x_lim = (extent[0]+extent[1])/2 + Dx*numpy.array([1,-1])/2
# Dy = (extent[2]-extent[3])*1.1
# y_lim = (extent[2]+extent[3])/2 + Dy*numpy.array([-1,1])/2
# ax.set_xlim(x_lim)
# ax.set_ylim(y_lim)
snr_lim = numpy.power(10., growth_lim(numpy.ma.log10(snr), 0.90, fac=1.05))
snr_lim[0] = max(0.1, snr_lim[0])
r68_lim = numpy.power(10., growth_lim(numpy.ma.log10(r68), 0.90, fac=1.05))
r99_lim = numpy.power(10., growth_lim(numpy.ma.log10(r99), 0.90, fac=1.05))
chi_lim = growth_lim(rchi2, 0.90, fac=1.05)
s_lim = numpy.power(10., growth_lim(numpy.ma.log10(signal), 0.99,
fac=1.05))
a_lim = growth_lim(numpy.ma.log10(a), 0.99, fac=1.05)
da_lim = growth_lim(numpy.ma.log10(da), 0.99, fac=1.05)
an_lim = growth_lim(numpy.ma.log10(an), 0.99, fac=1.05)
dan_lim = growth_lim(numpy.ma.log10(dan), 0.99, fac=1.05)
gmr_lim = None if gmr is None else growth_lim(gmr, 0.95, fac=1.10)
t_lim = growth_lim(numpy.ma.log10(t), 0.99, fac=1.05)
dt_lim = growth_lim(numpy.ma.log10(dt), 0.99, fac=1.05)
tn_lim = growth_lim(numpy.ma.log10(tn), 0.99, fac=1.05)
dtn_lim = growth_lim(numpy.ma.log10(dtn), 0.99, fac=1.05)
sv_lim = growth_lim(svel, 0.90, fac=1.2, midpoint=0)
so_lim = numpy.power(10., growth_lim(numpy.ma.log10(ssigo), 0.90,
fac=1.10))
sc_lim = growth_lim(ssigcor, 0.90, fac=1.10)
ss_lim = numpy.power(10., growth_lim(numpy.ma.log10(ssigc), 0.90,
fac=1.10))
so_lim = [min(so_lim[0], ss_lim[0]), max(so_lim[1], ss_lim[1])]
ss_lim = so_lim.copy()
# so_lim = [10,400]
# ss_lim = [10,400]
# ------------------------------------------------------------------
# Layout
left = 0.035
bott = 0.08
imwd = 0.18
hbuf = 0.06
cbuf = 0.005
cbwd = 0.01
vbuf = 0.03
# ------------------------------------------------------------------
# ------------------------------------------------------------------
# S/N
i, j = 0, 3
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
# print(numpy.sum(snr.mask), numpy.prod(snr.shape), numpy.sum(snr.mask)/numpy.prod(snr.shape))
masked_imshow(fig,
ax,
cax,
snr,
extent=extent,
norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]),
cmap='viridis',
zorder=3,
cbformat='%.1f',
subs=(1., 2., 4.))
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'S/N',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# 68th percentile of fractional residuals
i, j = 1, 3
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
# print(numpy.sum(r68.mask), numpy.prod(r68.shape), numpy.sum(r68.mask)/numpy.prod(r68.shape))
masked_imshow(fig,
ax,
cax,
r68,
extent=extent,
norm=colors.LogNorm(vmin=r68_lim[0], vmax=r68_lim[1]),
cmap='viridis_r',
zorder=3,
cbformat='%.2f',
subs=(1., 2., 4.))
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'68% Frac. Resid',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ax.text(0.5, 1.2, '{0}-{1}'.format(plt, ifu), horizontalalignment='center',
# verticalalignment='center', transform=ax.transAxes, fontsize=12)
# ax.text(0.5, 1.08, daptype, horizontalalignment='center',
# verticalalignment='center', transform=ax.transAxes, fontsize=12)
# ------------------------------------------------------------------
# 99th percentile of fractional residuals
i, j = 2, 3
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
# print(numpy.sum(r99.mask), numpy.prod(r99.shape), numpy.sum(r99.mask)/numpy.prod(r99.shape))
masked_imshow(fig,
ax,
cax,
r99,
extent=extent,
norm=colors.LogNorm(vmin=r99_lim[0], vmax=r99_lim[1]),
cmap='viridis_r',
zorder=3,
cbformat='%.2f',
subs=(1., 2., 4.))
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'99% Frac. Resid',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Reduced chi-square
i, j = 3, 3
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
# print(numpy.sum(rchi2.mask), numpy.prod(rchi2.shape),
# numpy.sum(rchi2.mask)/numpy.prod(rchi2.shape))
masked_imshow(fig,
ax,
cax,
rchi2,
extent=extent,
vmin=chi_lim[0],
vmax=chi_lim[1],
cmap='viridis_r',
zorder=3,
cbformat='%.2f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\chi^2_\nu$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Signal
i, j = 0, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
# print(numpy.sum(signal.mask), numpy.prod(signal.shape),
# numpy.sum(signal.mask)/numpy.prod(signal.shape))
masked_imshow(fig,
ax,
cax,
signal,
extent=extent,
norm=colors.LogNorm(vmin=s_lim[0], vmax=s_lim[1]),
cmap='viridis',
zorder=3,
cbformat='%.1f',
subs=(1., 2., 4.))
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$I_g$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# A
i, j = 1, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
lga = numpy.ma.log10(a)
# print(numpy.sum(lga.mask), numpy.prod(lga.shape), numpy.sum(lga.mask)/numpy.prod(lga.shape))
masked_imshow(fig,
ax,
cax,
lga,
extent=extent,
vmin=a_lim[0],
vmax=a_lim[1],
cmap='viridis',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\log A$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Delta A
i, j = 2, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
lgan = numpy.ma.log10(an)
# print(numpy.sum(lgan.mask), numpy.prod(lgan.shape), numpy.sum(lgan.mask)/numpy.prod(lgan.shape))
masked_imshow(fig,
ax,
cax,
lgan,
extent=extent,
vmin=an_lim[0],
vmax=an_lim[1],
cmap='viridis_r',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\log A_n$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Delta A/Ig
i, j = 3, 2
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
lgdan = numpy.ma.log10(dan)
# print(numpy.sum(lgdan.mask), numpy.prod(lgdan.shape),
# numpy.sum(lgdan.mask)/numpy.prod(lgdan.shape))
masked_imshow(fig,
ax,
cax,
lgdan,
extent=extent,
vmin=dan_lim[0],
vmax=dan_lim[1],
cmap='viridis_r',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\log(\delta A_n)$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# g-r Color
i, j = 0, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
# print(numpy.sum(gmr.mask), numpy.prod(gmr.shape), numpy.sum(gmr.mask)/numpy.prod(gmr.shape))
masked_imshow(fig,
ax,
cax,
gmr,
extent=extent,
vmin=gmr_lim[0],
vmax=gmr_lim[1],
cmap='RdBu_r',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$g-r$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# T
i, j = 1, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
lgt = numpy.ma.log10(t)
# print(numpy.sum(lgt.mask), numpy.prod(lgt.shape), numpy.sum(lgt.mask)/numpy.prod(lgt.shape))
masked_imshow(fig,
ax,
cax,
lgt,
extent=extent,
vmin=t_lim[0],
vmax=t_lim[1],
cmap='viridis',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\log T$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Delta T
i, j = 2, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
lgtn = numpy.ma.log10(tn)
# print(numpy.sum(lgtn.mask), numpy.prod(lgtn.shape), numpy.sum(lgtn.mask)/numpy.prod(lgtn.shape))
masked_imshow(fig,
ax,
cax,
lgtn,
extent=extent,
vmin=tn_lim[0],
vmax=tn_lim[1],
cmap='viridis_r',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\log T_n$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Delta T/Ig
i, j = 3, 1
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
lgdtn = numpy.ma.log10(dtn)
# print(numpy.sum(lgdtn.mask), numpy.prod(lgdtn.shape),
# numpy.sum(lgdtn.mask)/numpy.prod(lgdtn.shape))
masked_imshow(fig,
ax,
cax,
lgdtn,
extent=extent,
vmin=dtn_lim[0],
vmax=dtn_lim[1],
cmap='viridis_r',
zorder=3,
cbformat='%.1f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\log \delta T_n$',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Stellar velocity
i, j = 0, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
# print(numpy.sum(svel.mask), numpy.prod(svel.shape), numpy.sum(svel.mask)/numpy.prod(svel.shape))
masked_imshow(fig,
ax,
cax,
svel,
extent=extent,
vmin=sv_lim[0],
vmax=sv_lim[1],
cmap='RdBu_r',
zorder=3,
cbformat='%.0f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$V_\ast$ (km/s)',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Stellar sigma
i, j = 1, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
# print(numpy.sum(ssigo.mask), numpy.prod(ssigo.shape),
# numpy.sum(ssigo.mask)/numpy.prod(ssigo.shape))
masked_imshow(fig,
ax,
cax,
ssigo,
extent=extent,
norm=colors.LogNorm(vmin=so_lim[0], vmax=so_lim[1]),
cmap='viridis',
zorder=3,
cbformat='%.0f',
subs=(1., 2., 4.))
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\sigma_{\rm obs}$ (km/s)',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Sigma correction
i, j = 2, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
# print(numpy.sum(ssigcor.mask), numpy.prod(ssigcor.shape),
# numpy.sum(ssigcor.mask)/numpy.prod(ssigcor.shape))
masked_imshow(fig,
ax,
cax,
ssigcor,
extent=extent,
vmin=sc_lim[0],
vmax=sc_lim[1],
cmap='viridis',
zorder=3,
cbformat='%.0f')
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\sigma_{\rm corr}$ (km/s)',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# ------------------------------------------------------------------
# Corrected stellar sigma
i, j = 3, 0
ax = init_image_ax(
fig, [left + i * (imwd + hbuf), bott + j * (imwd + vbuf), imwd, imwd])
cax = fig.add_axes([
left + i * (imwd + hbuf) + imwd + cbuf, bott + j * (imwd + vbuf), cbwd,
imwd
])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
# print(numpy.sum(ssigc.mask), numpy.prod(ssigc.shape),
# numpy.sum(ssigc.mask)/numpy.prod(ssigc.shape))
masked_imshow(fig,
ax,
cax,
ssigo,
extent=extent,
norm=colors.LogNorm(vmin=ss_lim[0], vmax=ss_lim[1]),
cmap='viridis',
zorder=3,
cbformat='%.0f',
subs=(1., 2., 4.))
ax.set_xlim(extent[:2])
ax.set_ylim(extent[2:])
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.1,
0.95,
r'$\sigma_\ast$ (km/s)',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
if ofile is None:
pyplot.show()
else:
print('Writing: {0}'.format(ofile))
fig.canvas.print_figure(ofile, bbox_inches='tight')
fig.clear()
pyplot.close(fig)
def radial_coverage_histogram(dapall, plot_file=None):
# Get the range
arcsec_rng = growth_lim(dapall['RCOV90'], 0.99, fac=1.1)
rore = numpy.ma.divide(dapall['RCOV90'], dapall['NSA_ELPETRO_TH50_R'])
rore[dapall['NSA_ELPETRO_TH50_R'] < 0] = numpy.ma.masked
rore_rng = growth_lim(rore, 0.99, fac=1.1)
# Determine the sample
sdssMaskbits = sdss_maskbits_file()
targ1bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
ancillary = dapall['MNGTARG3'] > 0
primaryplus = targ1bm.flagged(dapall['MNGTARG1'],
flag=['PRIMARY_v1_2_0', 'COLOR_ENHANCED_v1_2_0'])
secondary = targ1bm.flagged(dapall['MNGTARG1'], flag='SECONDARY_v1_2_0')
other = numpy.invert(ancillary) & numpy.invert(primaryplus) & numpy.invert(secondary)
indx = dapall['RCOV90'] < 1
print('Num. of observations with 90% coverage <1 arcsec: {0}'.format(numpy.sum(indx)))
print(dapall['PLATEIFU'][indx])
# Instantiate the figure
w,h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5*w,1.5*h))
nlim = [0.8, 1000]
# Show RCOV90 in terms of arcseconds for each IFU
ax = init_ax(fig, [0.1, 0.58, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonposy='clip')
ifu = [ 19, 37, 61, 91, 127 ]
for i,f in enumerate(ifu):
indx = (dapall['IFUDESIGN']/100).astype(int) == f
ax.hist(dapall['RCOV90'][indx], bins=50, range=arcsec_rng, alpha=0.5,
histtype='stepfilled', color='C{0}'.format(i), zorder=2)
ax.hist(dapall['RCOV90'][indx], bins=50, range=arcsec_rng,
histtype='step', color='C{0}'.format(i), zorder=3)
medp = numpy.median(dapall['RCOV90'][indx])
ax.plot([medp, medp], nlim, color='C{0}'.format(i), zorder=4, linestyle='--')
ax.text(-0.1, 0.5, 'N', ha='center', va='center', transform=ax.transAxes, rotation='vertical')
ax.text(0.5, -0.13, r'$R_{90}$ (arcsec)', ha='center', va='center', transform=ax.transAxes)
ax.text(0.05, 0.95, '19-fiber', color='C0', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.90, '37-fiber', color='C1', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.85, '61-fiber', color='C2', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.80, '91-fiber', color='C3', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.75, '127-fiber', color='C4', ha='left', va='center', transform=ax.transAxes)
# Show RCOV90 in terms of Re for each sample
ax = init_ax(fig, [0.1, 0.08, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonposy='clip')
ax.hist(rore[primaryplus], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C0', zorder=2)
ax.hist(rore[primaryplus], bins=50, range=rore_rng, histtype='step',
color='C0', zorder=3)
medp = numpy.median(rore[primaryplus])
ax.plot([medp, medp], nlim, color='C0', zorder=4, linestyle='--')
ax.hist(rore[secondary], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C1', zorder=2)
ax.hist(rore[secondary], bins=50, range=rore_rng, histtype='step',
color='C1', zorder=3)
medp = numpy.median(rore[secondary])
ax.plot([medp, medp], nlim, color='C1', zorder=4, linestyle='--')
ax.hist(rore[other], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C2', zorder=2)
ax.hist(rore[other], bins=50, range=rore_rng, histtype='step',
color='C2', zorder=3)
ax.hist(rore[ancillary], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C3', zorder=2)
ax.hist(rore[ancillary], bins=50, range=rore_rng, histtype='step',
color='C3', zorder=3)
ax.text(-0.1, 0.5, 'N', ha='center', va='center', transform=ax.transAxes, rotation='vertical')
ax.text(0.5, -0.12, r'$R_{90}/R_{eff}$', ha='center', va='center', transform=ax.transAxes)
ax.text(0.05, 0.95, 'Primary+', color='C0', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.90, 'Secondary', color='C1', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.85, 'Other', color='C2', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.80, 'Ancillary', color='C3', ha='left', va='center', transform=ax.transAxes)
if plot_file is None:
pyplot.show()
else:
fig.canvas.print_figure(plot_file, bbox_inches='tight')
fig.clear()
pyplot.close(fig)
def spotcheck_images(analysis_path,
daptype,
plate,
ifudesign,
ofile=None,
drpver=None,
dapver=None):
bm = DAPMapsBitMask()
plan_dir = defaults.dap_method_path(daptype,
plate=plate,
ifudesign=ifudesign,
drpver=drpver,
dapver=dapver,
analysis_path=analysis_path)
ifile = os.path.join(
plan_dir,
'manga-{0}-{1}-MAPS-{2}.fits.gz'.format(plate, ifudesign, daptype))
if not os.path.isfile(ifile):
raise FileNotFoundError('No file: {0}'.format(ifile))
print('Reading: {0}'.format(ifile))
hdu = fits.open(ifile)
# Check everything is finite
num_ext = len(hdu)
for i in range(num_ext):
if hdu[i].data is None:
continue
if not numpy.all(numpy.isfinite(hdu[i].data)):
raise ValueError('HDU {0} contains infs or NaNs!'.format(
hdu[i].name))
# Build the column dictionaries
emline = channel_dictionary(hdu, 'EMLINE_GFLUX')
specindex = channel_dictionary(hdu, 'SPECINDEX')
extent = map_extent(hdu, 'SPX_MFLUX')
# Get the data to plot
spxflx = numpy.ma.MaskedArray(hdu['SPX_MFLUX'].data.copy())
spxsnr = numpy.ma.MaskedArray(hdu['SPX_SNR'].data.copy())
binid = numpy.ma.MaskedArray(hdu['BINID'].data.copy(),
mask=hdu['BINID'].data < 0)
binsnr = numpy.ma.MaskedArray(hdu['BIN_SNR'].data.copy(),
mask=hdu['BIN_MFLUX_MASK'].data > 0)
# scfres = numpy.ma.MaskedArray(hdu['STELLAR_CONT_FRESID'].data.copy()[0,:,:],
# mask=numpy.invert(hdu['STELLAR_CONT_FRESID'].data[0,:,:] > 0))
# #bm.flagged(hdu['STELLAR_VEL_MASK'].data, 'DONOTUSE'))
# scrchi = numpy.ma.MaskedArray(hdu['STELLAR_CONT_RCHI2'].data.copy(),
# mask=numpy.invert(hdu['STELLAR_CONT_RCHI2'].data > 0))
# #mask=bm.flagged(hdu['STELLAR_VEL_MASK'].data, 'DONOTUSE'))
# 68% growth of the absolute value of the fractional residuals
scfres = numpy.ma.MaskedArray(
hdu['STELLAR_FOM'].data.copy()[3, :, :],
mask=numpy.invert(hdu['STELLAR_FOM'].data[3, :, :] > 0))
# Reduced chi-square
scrchi = numpy.ma.MaskedArray(
hdu['STELLAR_FOM'].data.copy()[2, :, :],
mask=numpy.invert(hdu['STELLAR_FOM'].data[2, :, :] > 0))
strvel = numpy.ma.MaskedArray(hdu['STELLAR_VEL'].data.copy(),
mask=bm.flagged(hdu['STELLAR_VEL_MASK'].data,
'DONOTUSE'))
ustrvel = numpy.ma.MaskedArray(hdu['STELLAR_VEL'].data.copy(),
mask=numpy.invert(
bm.flagged(hdu['STELLAR_VEL_MASK'].data,
'UNRELIABLE')))
ustrvel[numpy.invert(numpy.ma.getmaskarray(ustrvel))] = 0.0
strsig = numpy.ma.MaskedArray(hdu['STELLAR_SIGMA'].data.copy(),
mask=bm.flagged(
hdu['STELLAR_SIGMA_MASK'].data,
'DONOTUSE'))
ustrsig = numpy.ma.MaskedArray(hdu['STELLAR_SIGMA'].data.copy(),
mask=numpy.invert(
bm.flagged(
hdu['STELLAR_SIGMA_MASK'].data,
'UNRELIABLE')))
ustrsig[numpy.invert(numpy.ma.getmaskarray(ustrsig))] = 0.0
hasflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhasflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhasflx[numpy.invert(numpy.ma.getmaskarray(uhasflx))] = 0.0
hagflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhagflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhagflx[numpy.invert(numpy.ma.getmaskarray(uhagflx))] = 0.0
hagvel = numpy.ma.MaskedArray(
hdu['EMLINE_GVEL'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(hdu['EMLINE_GVEL_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhagvel = numpy.ma.MaskedArray(
hdu['EMLINE_GVEL'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GVEL_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhagvel[numpy.invert(numpy.ma.getmaskarray(uhagvel))] = 0.0
hagsig = numpy.ma.MaskedArray(
hdu['EMLINE_GSIGMA'].data.copy()[emline['Ha-6564'], :, :],
mask=bm.flagged(
hdu['EMLINE_GSIGMA_MASK'].data[emline['Ha-6564'], :, :],
'DONOTUSE'))
uhagsig = numpy.ma.MaskedArray(
hdu['EMLINE_GSIGMA'].data.copy()[emline['Ha-6564'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GSIGMA_MASK'].data[emline['Ha-6564'], :, :],
'UNRELIABLE')))
uhagsig[numpy.invert(numpy.ma.getmaskarray(uhagsig))] = 0.0
hbsflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Hb-4862'], :, :],
'DONOTUSE'))
uhbsflx = numpy.ma.MaskedArray(
hdu['EMLINE_SFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_SFLUX_MASK'].data[emline['Hb-4862'], :, :],
'UNRELIABLE')))
uhbsflx[numpy.invert(numpy.ma.getmaskarray(uhbsflx))] = 0.0
hbgflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Hb-4862'], :, :],
'DONOTUSE'))
uhbgflx = numpy.ma.MaskedArray(
hdu['EMLINE_GFLUX'].data.copy()[emline['Hb-4862'], :, :],
mask=numpy.invert(
bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[emline['Hb-4862'], :, :],
'UNRELIABLE')))
uhbgflx[numpy.invert(numpy.ma.getmaskarray(uhbgflx))] = 0.0
specindex_available = True
try:
if hdu['SPECINDEX'].data is None:
pass
except:
warnings.warn('No spectral indices in maps file.')
specindex_available = False
if specindex_available:
d4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['D4000'], :, :],
mask=bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['D4000'], :, :],
'DONOTUSE'))
ud4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['D4000'], :, :],
mask=numpy.invert(
bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['D4000'], :, :],
'UNRELIABLE')))
ud4000[numpy.invert(numpy.ma.getmaskarray(ud4000))] = 0.0
dn4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['Dn4000'], :, :],
mask=bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['Dn4000'], :, :],
'DONOTUSE'))
udn4000 = numpy.ma.MaskedArray(
hdu['SPECINDEX'].data.copy()[specindex['Dn4000'], :, :],
mask=numpy.invert(
bm.flagged(
hdu['SPECINDEX_MASK'].data[specindex['Dn4000'], :, :],
'UNRELIABLE')))
udn4000[numpy.invert(numpy.ma.getmaskarray(udn4000))] = 0.0
else:
output_shape = spxflx.shape
d4000 = numpy.ma.masked_all(output_shape)
ud4000 = numpy.ma.masked_all(output_shape)
dn4000 = numpy.ma.masked_all(output_shape)
udn4000 = numpy.ma.masked_all(output_shape)
# Get the limits to apply
flux_lim = numpy.power(10,
growth_lim(numpy.ma.log10(spxflx), 0.90, fac=1.05))
t = numpy.ma.append(numpy.ma.log10(spxsnr), numpy.ma.log10(binsnr))
snr_lim = numpy.power(10, growth_lim(t, 0.90, fac=1.05))
bin_lim = [numpy.ma.amin(binid), numpy.ma.amax(binid)]
res_lim = numpy.power(10, growth_lim(numpy.ma.log10(scfres),
0.90,
fac=1.05))
chi_lim = growth_lim(scrchi, 0.90, fac=1.05)
t = numpy.ma.append(strvel, hagvel)
vel_lim = growth_lim(t, 0.85, fac=1.10, midpoint=0.0)
if vel_lim[0] < -350:
vel_lim[0] = -350
if vel_lim[1] > 350:
vel_lim[1] = 350
t = numpy.ma.append(numpy.ma.log10(strsig), numpy.ma.log10(hagsig))
sig_lim = numpy.power(10, growth_lim(t, 0.90, fac=1.05))
t = numpy.ma.append(
numpy.ma.append(numpy.ma.log10(hasflx), numpy.ma.log10(hagflx)),
numpy.ma.append(numpy.ma.log10(hbsflx), numpy.ma.log10(hbgflx)))
# t = numpy.ma.log10(t)
hflx_lim = numpy.power(10, growth_lim(t, 0.95, fac=1.10))
# hflx_lim = [ -0.3, 2.2 ]
t = numpy.ma.append(d4000, dn4000)
# d4000_lim = growth_lim(t, 0.90, 1.05)
d4000_lim = [1.0, 2.5]
font = {'size': 6}
rc('font', **font)
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
dx = 0.22
left = 0.05
top = 0.94
dw = 0.005
snr_levels = [0.5, 1.0, 1.5, 2.0]
ax = init_ax(fig, [left, top - dx, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([left + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [flux_lim[0] * 1.1, flux_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
spxflx,
extent=extent,
norm=colors.LogNorm(vmin=flux_lim[0], vmax=flux_lim[1]),
cmap='YlGnBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter())
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'g-band S (spx)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - dx, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + dx + dw + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [snr_lim[0] * 1.1, snr_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
spxsnr,
extent=extent,
norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]),
cmap='YlGnBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'g-band S/N (spx)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax.text(1.0,
1.1,
r'{0}-{1}; {2}'.format(plate, ifudesign,
hdu['PRIMARY'].header['MANGAID']),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=20)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - dx, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + 2 * dx + 2 * dw + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
masked_imshow(fig,
ax,
cax,
binid[0, :, :],
extent=extent,
vmin=bin_lim[0],
vmax=bin_lim[1],
cmap='CMRmap',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbformat=ticker.ScalarFormatter(),
cbticks=bin_lim)
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'bin ID',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - dx, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + 3 * dx + 3 * dw + dx * 0.65, top - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [snr_lim[0] * 1.1, snr_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
binsnr,
extent=extent,
norm=colors.LogNorm(vmin=snr_lim[0], vmax=snr_lim[1]),
cmap='YlGnBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'g-band S/N (bin)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left, top - 2 * dx - dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + dx * 0.65, top - dx - dw - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [res_lim[0] * 1.1, res_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
scfres,
extent=extent,
norm=colors.LogNorm(vmin=res_lim[0], vmax=res_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'pPXF Frac. Resid.',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - 2 * dx - dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + dx + dw + dx * 0.65, top - dx - dw - 0.07 * dx, 0.27 * dx, 0.01
])
cbticks = [
numpy.round(chi_lim[0] + 0.01, decimals=2),
numpy.round(chi_lim[1] - 0.01, decimals=2)
]
masked_imshow(fig,
ax,
cax,
scrchi,
extent=extent,
vmin=chi_lim[0],
vmax=chi_lim[1],
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'${\rm pPXF}\ \chi^2_\nu$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - 2 * dx - dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 2 * dx + 2 * dw + dx * 0.65, top - dx - dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [vel_lim[0] / 1.1, 0.0, vel_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
strvel,
extent=extent,
vmin=vel_lim[0],
vmax=vel_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(strvel, origin='lower', interpolation='nearest', cmap='RdBu_r',
# zorder=4, extent=extent, vmin=vel_lim[0], vmax=vel_lim[1])
# im2 = ax.imshow(ustrvel, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(vel_lim[0]+1), numpy.round(vel_lim[1]-1)], format='%.0f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'$V_\ast$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - 2 * dx - dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 3 * dx + 3 * dw + dx * 0.65, top - dx - dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [
sig_lim[0] * 1.1,
numpy.power(10, numpy.mean(numpy.log10(sig_lim))), sig_lim[1] / 1.1
]
masked_imshow(fig,
ax,
cax,
strsig,
extent=extent,
norm=colors.LogNorm(vmin=sig_lim[0], vmax=sig_lim[1]),
cmap='viridis',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(numpy.ma.log10(strsig), origin='lower', interpolation='nearest',
# cmap='viridis', zorder=4, extent=extent, vmin=sig_lim[0], vmax=sig_lim[1])
# im2 = ax.imshow(ustrsig, origin='lower', interpolation='nearest', cmap='Reds_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(sig_lim[0]+0.1, decimals=1),
# numpy.round(sig_lim[1]-0.1, decimals=1)], format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'$\sigma_\ast$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left, top - 3 * dx - 2 * dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes(
[left + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hasflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(hasflx, origin='lower', interpolation='nearest', cmap='inferno',
# zorder=4, extent=extent, vmin=hflx_lim[0], vmax=hflx_lim[1])
# im2 = ax.imshow(uhasflx, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(hflx_lim[0]+0.1, decimals=1),
# numpy.round(hflx_lim[1]-0.1, decimals=1)], format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\alpha\ {\rm flux}$ (sum)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - 3 * dx - 2 * dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + dx + dw + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hagflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(hagflx, origin='lower', interpolation='nearest', cmap='inferno',
# zorder=4, extent=extent, vmin=hflx_lim[0], vmax=hflx_lim[1])
# im2 = ax.imshow(uhagflx, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(hflx_lim[0]+0.1, decimals=1),
# numpy.round(hflx_lim[1]-0.1, decimals=1)], format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\alpha\ {\rm flux}$ (Gauss)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - 3 * dx - 2 * dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 2 * dx + 2 * dw + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [vel_lim[0] / 1.1, 0.0, vel_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hagvel,
extent=extent,
vmin=vel_lim[0],
vmax=vel_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(hagvel, origin='lower', interpolation='nearest', cmap='RdBu_r',
# zorder=4, extent=extent, vmin=vel_lim[0], vmax=vel_lim[1])
# im2 = ax.imshow(uhagvel, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(vel_lim[0]+1), numpy.round(vel_lim[1]-1)], format='%.0f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'$V_{{\rm H}\alpha}$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - 3 * dx - 2 * dw, dx, dx])
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 3 * dx + 3 * dw + dx * 0.65, top - 2 * dx - 2 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [
sig_lim[0] * 1.1,
numpy.power(10, numpy.mean(numpy.log10(sig_lim))), sig_lim[1] / 1.1
]
masked_imshow(fig,
ax,
cax,
hagsig,
extent=extent,
norm=colors.LogNorm(vmin=sig_lim[0], vmax=sig_lim[1]),
cmap='viridis',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(numpy.ma.log10(hagsig), origin='lower', interpolation='nearest',
# cmap='viridis', zorder=4, extent=extent, vmin=sig_lim[0], vmax=sig_lim[1])
# im2 = ax.imshow(uhagsig, origin='lower', interpolation='nearest', cmap='Reds_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(sig_lim[0]+0.1, decimals=1),
# numpy.round(sig_lim[1]-0.1, decimals=1)], format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'$\sigma_{{\rm H}\alpha}$',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left, top - 4 * dx - 3 * dw, dx, dx])
cax = fig.add_axes(
[left + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx, 0.27 * dx, 0.01])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hbsflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(hbsflx, origin='lower', interpolation='nearest', cmap='inferno',
# zorder=4, extent=extent, vmin=hflx_lim[0], vmax=hflx_lim[1])
# im2 = ax.imshow(uhbsflx, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(hflx_lim[0]+0.1, decimals=1),
# numpy.round(hflx_lim[1]-0.1, decimals=1)], format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\beta\ {\rm flux}$ (sum)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + dx + dw, top - 4 * dx - 3 * dw, dx, dx])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + dx + dw + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
cbticks = [hflx_lim[0] * 1.1, hflx_lim[1] / 1.1]
masked_imshow(fig,
ax,
cax,
hbgflx,
extent=extent,
norm=colors.LogNorm(vmin=hflx_lim[0], vmax=hflx_lim[1]),
cmap='inferno',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=cbticks,
cbformat=ticker.ScalarFormatter()) #'%.0f')
# im = ax.imshow(hbgflx, origin='lower', interpolation='nearest', cmap='inferno',
# zorder=4, extent=extent, vmin=hflx_lim[0], vmax=hflx_lim[1])
# im2 = ax.imshow(uhbgflx, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal',
# ticks=[numpy.round(hflx_lim[0]+0.1, decimals=1),
# numpy.round(hflx_lim[1]-0.1, decimals=1)], format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'${\rm H}\beta\ {\rm flux}$ (Gauss)',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 2 * dx + 2 * dw, top - 4 * dx - 3 * dw, dx, dx])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 2 * dx + 2 * dw + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
masked_imshow(fig,
ax,
cax,
d4000,
extent=extent,
vmin=d4000_lim[0],
vmax=d4000_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=d4000_lim,
cbformat=ticker.ScalarFormatter())
# im = ax.imshow(d4000, origin='lower', interpolation='nearest', cmap='RdBu_r',
# zorder=4, extent=extent, vmin=d4000_lim[0], vmax=d4000_lim[1])
# im2 = ax.imshow(ud4000, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal', ticks=d4000_lim, format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'D4000',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
ax = init_ax(fig, [left + 3 * dx + 3 * dw, top - 4 * dx - 3 * dw, dx, dx])
ax.yaxis.set_major_formatter(ticker.NullFormatter())
cax = fig.add_axes([
left + 3 * dx + 3 * dw + dx * 0.65, top - 3 * dx - 3 * dw - 0.07 * dx,
0.27 * dx, 0.01
])
masked_imshow(fig,
ax,
cax,
dn4000,
extent=extent,
vmin=d4000_lim[0],
vmax=d4000_lim[1],
cmap='RdBu_r',
zorder=4,
orientation='horizontal',
contour_data=numpy.ma.log10(spxsnr),
levels=snr_levels,
cbticks=d4000_lim,
cbformat=ticker.ScalarFormatter())
# im = ax.imshow(dn4000, origin='lower', interpolation='nearest', cmap='RdBu_r',
# zorder=4, extent=extent, vmin=d4000_lim[0], vmax=d4000_lim[1])
# im2 = ax.imshow(udn4000, origin='lower', interpolation='nearest', cmap='Greens_r',
# vmin=0, vmax=1, zorder=5, alpha=0.3, extent=extent)
# cnt = ax.contour(numpy.ma.log10(spxsnr), origin='lower', extent=im.get_extent(),
# colors='0.5', levels=snr_levels, linewidths=1.5, zorder=6)
# pyplot.colorbar(im, cax=cax, orientation='horizontal', ticks=d4000_lim, format='%.1f')
ax.add_patch(
map_beam_patch(extent, ax, facecolor='0.7', edgecolor='k', zorder=4))
ax.text(0.99,
0.05,
r'Dn4000',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes)
if ofile is not None:
fig.canvas.print_figure(ofile, bbox_inches='tight')
print('Writing: {0}'.format(ofile))
else:
pyplot.show()
fig.clear()
pyplot.close(fig)
