def get_emission_line_fom_maps(dapmaps):
maps_hdu = fits.open(dapmaps)
eml = channel_dictionary(maps_hdu, 'EMLINE_GFLUX')
anr = numpy.ma.MaskedArray(
maps_hdu['EMLINE_GANR'].data[eml['Ha-6564'], ...],
mask=maps_hdu['EMLINE_GFLUX_MASK'].data[eml['Ha-6564'], ...] > 0)
ha = numpy.ma.MaskedArray(
maps_hdu['EMLINE_GFLUX'].data[eml['Ha-6564'], ...],
mask=maps_hdu['EMLINE_GFLUX_MASK'].data[eml['Ha-6564'], ...] > 0)
hb = numpy.ma.MaskedArray(
maps_hdu['EMLINE_GFLUX'].data[eml['Hb-4862'], ...],
mask=maps_hdu['EMLINE_GFLUX_MASK'].data[eml['Hb-4862'], ...] > 0)
aob = numpy.ma.divide(ha, hb)
# Get the associated S/N and fit metrics
rms = numpy.ma.MaskedArray(maps_hdu['EMLINE_FOM'].data[0, :, :])
rms[numpy.invert(rms > 0)] = numpy.ma.masked
frms = numpy.ma.MaskedArray(maps_hdu['EMLINE_FOM'].data[1, :, :])
frms[numpy.invert(frms > 0)] = numpy.ma.masked
rchi2 = numpy.ma.MaskedArray(maps_hdu['EMLINE_FOM'].data[2, :, :])
rchi2[numpy.invert(rchi2 > 0)] = numpy.ma.masked
chi_grw = numpy.ma.MaskedArray(maps_hdu['EMLINE_FOM'].data[6:, :, :])
chi_grw[numpy.invert(chi_grw > 0)] = numpy.ma.masked
extent = map_extent(maps_hdu, 'SPX_MFLUX')
return extent, anr, aob, rms, frms, rchi2, chi_grw
def get_stellar_continuum_fom_maps(drpcube, dapmaps):
if not os.path.isfile(dapmaps):
raise FileNotFoundError('{0} does not exist.'.format(dapmaps))
# Get the associated S/N and fit metrics
maps_hdu = fits.open(dapmaps)
snrg = numpy.ma.MaskedArray(
maps_hdu['BIN_SNR'].data,
mask=numpy.invert(maps_hdu['BIN_SNR'].data > 0))
mask = maps_hdu['STELLAR_VEL_MASK'].data > 0
rms = numpy.ma.MaskedArray(maps_hdu['STELLAR_FOM'].data[0, :, :],
mask=mask)
frms = numpy.ma.MaskedArray(maps_hdu['STELLAR_FOM'].data[1, :, :],
mask=mask)
rchi2 = numpy.ma.MaskedArray(maps_hdu['STELLAR_FOM'].data[2, :, :],
mask=mask)
chi_grw = numpy.ma.MaskedArray(maps_hdu['STELLAR_FOM'].data[6:, :, :])
chi_grw[0, mask] = numpy.ma.masked
chi_grw[1, mask] = numpy.ma.masked
chi_grw[2, mask] = numpy.ma.masked
extent = map_extent(maps_hdu, 'SPX_MFLUX')
return extent, snrg, gmr_data(drpcube), rms, frms, rchi2, chi_grw
def maps_data(plt, ifu, plan, drpver, dapver, analysis_path):
bin_method = SpatiallyBinnedSpectra.define_method(plan['bin_key'])
sc_method = StellarContinuumModel.define_method(plan['continuum_key'])
el_method = EmissionLineModel.define_method(plan['elfit_key'])
method = defaults.dap_method(bin_method['key'],
sc_method['fitpar']['template_library_key'],
el_method['continuum_tpl_key'])
directory_path = defaults.dap_method_path(method,
plate=plt,
ifudesign=ifu,
drpver=drpver,
dapver=dapver,
analysis_path=analysis_path)
maps_file = defaults.dap_file_name(plt, ifu, method, mode='MAPS')
with fits.open(os.path.join(directory_path, maps_file)) as hdu:
mask = hdu['BINID'].data[1, :, :] < 0
r68_map = numpy.ma.MaskedArray(hdu['STELLAR_FOM'].data[3, :, :],
mask=mask)
r99_map = numpy.ma.MaskedArray(hdu['STELLAR_FOM'].data[4, :, :],
mask=mask)
rchi2_map = numpy.ma.MaskedArray(hdu['STELLAR_FOM'].data[2, :, :],
mask=mask)
svel_map = numpy.ma.MaskedArray(hdu['STELLAR_VEL'].data[:, :],
mask=mask)
ssigo_map = numpy.ma.MaskedArray(hdu['STELLAR_SIGMA'].data[:, :],
mask=mask)
ssigcor_map = numpy.ma.MaskedArray(
hdu['STELLAR_SIGMACORR'].data[1, :, :], mask=mask)
ssigc_map = numpy.ma.sqrt(
numpy.square(ssigo_map) - numpy.square(ssigcor_map))
extent = map_extent(hdu, 'SPX_MFLUX')
return r68_map, r99_map, rchi2_map, svel_map, ssigo_map, ssigcor_map, ssigc_map, extent
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 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)