def main(args):
t = time.perf_counter()
if args.bgagg:
pyplot.switch_backend('agg')
redux_path = manga.drp_redux_path(drpver=args.drpver) \
if args.redux_path is None else Path(args.redux_path).resolve()
analysis_path = manga.dap_analysis_path(drpver=args.drpver, dapver=args.dapver) \
if args.analysis_path is None else Path(args.analysis_path).resolve()
plan = manga.MaNGAAnalysisPlan.default(analysis_path=analysis_path) \
if args.plan_file is None \
else manga.MaNGAAnalysisPlan.from_toml(args.plan_file,
analysis_path=analysis_path)
drpall_file = manga.drpall_file(drpver=args.drpver,
redux_path=redux_path)
dapall_file = manga.dapall_file(drpver=args.drpver,
dapver=args.dapver,
analysis_path=analysis_path)
drpall_hdu = fits.open(str(drpall_file))
dapall_hdu = fits.open(str(dapall_file))
eml = channel_dictionary(dapall_hdu, 0, prefix='ELG')
spi = channel_dictionary(dapall_hdu, 0, prefix='SPI')
for i, key in enumerate(plan.keys()):
dapall_qa(drpall_hdu['MANGA'].data,
dapall_hdu[plan[key]['key']].data, analysis_path,
plan[key]['key'], eml, spi)
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))
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 compile_data(dapver, analysis_path, daptype, plt):
ifus = [
1901, 1902, 3701, 3702, 3703, 3704, 6101, 6102, 6103, 6104, 9101, 9102,
12701, 12702, 12703, 12704, 12705
]
sc_pltifu = []
sc_snrg = []
sc_frms = []
sc_rchi = []
el_pltifu = []
el_snrg = []
el_frms = []
el_rchi = []
for ifu in ifus:
apath = os.path.join(analysis_path, daptype, str(plt), str(ifu))
maps_file = os.path.join(
apath, 'manga-{0}-{1}-MAPS-{2}.fits.gz'.format(plt, ifu, daptype))
if not os.path.isfile(maps_file):
continue
hdu = fits.open(maps_file)
# Get the stellar data
uniq, indx = map(
lambda x: x[1:],
numpy.unique(hdu['BINID'].data[1, :, :].ravel(),
return_index=True))
mask = hdu['STELLAR_VEL_MASK'].data.ravel()[indx] > 0
indx = indx[numpy.invert(mask)]
if len(indx) > 0:
sc_pltifu += ['{0}-{1}'.format(plt, ifu)]
sc_snrg += [hdu['BIN_SNR'].data.ravel()[indx]]
sc_frms += [hdu['STELLAR_FOM'].data[1, :, :].ravel()[indx]]
sc_rchi += [hdu['STELLAR_FOM'].data[2, :, :].ravel()[indx]]
# Get the emission-line data
eml = channel_dictionary(hdu, 'EMLINE_GFLUX')
uniq, indx = map(
lambda x: x[1:],
numpy.unique(hdu['BINID'].data[3, :, :].ravel(),
return_index=True))
mask = hdu['EMLINE_GFLUX_MASK'].data[
eml['Ha-6564'], :, :].ravel()[indx] > 0
indx = indx[numpy.invert(mask)]
if len(indx) > 0:
el_pltifu += ['{0}-{1}'.format(plt, ifu)]
el_snrg += [hdu['SPX_SNR'].data.ravel()[indx]]
el_frms += [hdu['EMLINE_FOM'].data[1, :, :].ravel()[indx]]
el_rchi += [hdu['EMLINE_FOM'].data[2, :, :].ravel()[indx]]
return ifus, numpy.array(sc_pltifu), sc_snrg, sc_frms, sc_rchi, numpy.array(el_pltifu), \
el_snrg, el_frms, el_rchi
def main(args):
t = time.perf_counter()
if args.bgagg:
pyplot.switch_backend('agg')
# Set the paths
redux_path = defaults.drp_redux_path(drpver=args.drpver) \
if args.redux_path is None else args.redux_path
analysis_path = defaults.dap_analysis_path(drpver=args.drpver, dapver=args.dapver) \
if args.analysis_path is None else args.analysis_path
daptypes = []
if args.daptype is None:
analysisplan = AnalysisPlanSet.default() if args.plan_file is None \
else AnalysisPlanSet.from_par_file(args.plan_file)
for p in analysisplan:
bin_method = SpatiallyBinnedSpectra.define_method(p['bin_key'])
sc_method = StellarContinuumModel.define_method(p['continuum_key'])
el_method = EmissionLineModel.define_method(p['elfit_key'])
daptypes += [
defaults.dap_method(
bin_method['key'],
sc_method['fitpar']['template_library_key'],
el_method['continuum_tpl_key'])
]
else:
daptypes = [args.daptype]
drpall_file = defaults.drpall_file(drpver=args.drpver,
redux_path=redux_path)
dapall_file = defaults.dapall_file(drpver=args.drpver,
dapver=args.dapver,
analysis_path=analysis_path)
drpall_hdu = fits.open(drpall_file)
dapall_hdu = fits.open(dapall_file)
eml = channel_dictionary(dapall_hdu, 0, prefix='ELG')
spi = channel_dictionary(dapall_hdu, 0, prefix='SPI')
for daptype in daptypes:
dapall_qa(drpall_hdu['MANGA'].data, dapall_hdu[daptype].data,
analysis_path, daptype, eml, spi)
print('Elapsed time: {0} seconds'.format(time.perf_counter() - t))
def ppxf_flag_info(tbl, maps_file):
qual_bm = DAPQualityBitMask()
bm = DAPMapsBitMask()
# # Targeting bits
# sdssMaskbits = defaults.sdss_maskbits_file()
# mngtarg1_bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
# mngtarg3_bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET3')
tbl['PLT'], tbl['IFU'] = map(lambda x : int(x), os.path.split(maps_file)[1].split('-')[1:3])
with fits.open(maps_file) as hdu:
# Check if the file was CRITICAL
tbl['CRIT'] = qual_bm.flagged(hdu['PRIMARY'].header['DAPQUAL'], flag='DRPCRIT')
tbl['MAIN'] = hdu[0].header['MNGTARG1'] > 0
el = channel_dictionary(hdu, 'EMLINE_GFLUX')
haflx = numpy.ma.MaskedArray(hdu['EMLINE_GFLUX'].data[el['Ha-6564']],
mask=hdu['EMLINE_GFLUX_MASK'].data[el['Ha-6564']] > 0)
tbl['HAMEAN'] = numpy.ma.median(haflx)
tbl['NSPX'] = haflx.size
tbl['NGOOD'] = numpy.sum(numpy.logical_not(numpy.ma.getmaskarray(haflx)))
indx = bm.flagged(hdu['EMLINE_GFLUX_MASK'].data[el['Ha-6564']], 'FITFAILED')
tbl['NFLG'] = numpy.sum(indx)
if not numpy.any(indx):
return
snr = numpy.ma.MaskedArray(hdu['SPX_SNR'].data,
mask=numpy.logical_not(hdu['SPX_SNR'].data > 0))
tbl['FLGSNR'] = numpy.ma.median(snr[indx])
srt = numpy.argsort(snr[indx])
tbl['EXSNR'] = snr[indx][srt][-1]
tbl['EXBIN'] = hdu['BINID'].data[3][indx][srt][-1]
flux = np.ma.MaskedArray(
hdu_cube['FLUX'].data,
mask=bm.flagged(hdu_cube['MASK'].data,
['IGNORED', 'FLUXINVALID', 'IVARINVALID', 'ARTIFACT']))
ivar = np.ma.MaskedArray(
hdu_cube['IVAR'].data,
mask=bm.flagged(hdu_cube['MASK'].data,
['IGNORED', 'FLUXINVALID', 'IVARINVALID', 'ARTIFACT']))
model = np.ma.MaskedArray(hdu_cube['MODEL'].data,
mask=bm.flagged(hdu_cube['MASK'].data, 'FITIGNORED'))
# Read in MAP
hdu_maps = fits.open(fits_maps_fil)
binid = hdu_maps['BINID'].data[1, :, :]
mask_ext = hdu_maps['EMLINE_GVEL'].header['QUALDATA']
emlc = channel_dictionary(hdu_maps, 'EMLINE_GVEL')
emission_vfield = np.ma.MaskedArray(
hdu_maps['EMLINE_GVEL'].data[emlc['Ha-6564'], :, :],
mask=hdu_maps[mask_ext].data[emlc['Ha-6564'], :, :] > 0)
emission_vfield_ivar = np.ma.MaskedArray(
hdu_maps['EMLINE_GVEL_IVAR'].data[emlc['Ha-6564'], :, :],
mask=hdu_maps[mask_ext].data[emlc['Ha-6564'], :, :] > 0)
mask_ext = hdu_maps['STELLAR_VEL'].header['QUALDATA']
stellar_vfield = np.ma.MaskedArray(hdu_maps['STELLAR_VEL'].data,
mask=hdu_maps[mask_ext].data > 0)
stellar_vfield_ivar = np.ma.MaskedArray(hdu_maps['STELLAR_VEL_IVAR'].data,
mask=hdu_maps[mask_ext].data > 0)
#Plot map of binids, emission line velocities, etc
fig, axes = pl.subplots(1, 3, figsize=(12, 4))
def get_halpha_data(plt, ifu, hdu):
el = channel_dictionary(hdu, 'EMLINE_GFLUX')
uniq_bins, uniq_indx, uniq_cnt = map(
lambda x: x[1:],
numpy.unique(hdu['BINID'].data[3].ravel(),
return_index=True,
return_counts=True))
keep = numpy.logical_not(hdu['EMLINE_GFLUX_MASK'].data.ravel()[uniq_indx] > 0) \
| numpy.logical_not(hdu['EMLINE_GEW_MASK'].data.ravel()[uniq_indx] > 0) \
| numpy.logical_not(hdu['EMLINE_GVEL_MASK'].data.ravel()[uniq_indx] > 0) \
| numpy.logical_not(hdu['EMLINE_GSIGMA_MASK'].data.ravel()[uniq_indx] > 0)
uniq_bins = uniq_bins[keep]
uniq_indx = uniq_indx[keep]
uniq_cnt = uniq_cnt[keep]
data = HalphaDataTable(shape=uniq_bins.shape)
data['PLATE'] = plt
data['IFU'] = ifu
data['Y'], data['X'] = numpy.unravel_index(uniq_indx,
hdu['BINID'].shape[1:])
data['BINID'] = uniq_bins
data['R'] = hdu['SPX_ELLCOO'].data[0].ravel()[uniq_indx]
data['R_RE'] = hdu['SPX_ELLCOO'].data[1].ravel()[uniq_indx]
data['R_KPC'] = hdu['SPX_ELLCOO'].data[2].ravel()[uniq_indx]
data['GCNT'] = hdu['SPX_MFLUX'].data.ravel()[uniq_indx]
data['GSNR'] = hdu['SPX_SNR'].data.ravel()[uniq_indx]
data['FLUX'] = hdu['EMLINE_GFLUX'].data[el['Ha-6564']].ravel()[uniq_indx]
data['FLUX_ERR'] = numpy.ma.power(
hdu['EMLINE_GFLUX_IVAR'].data[el['Ha-6564']].ravel()[uniq_indx],
-0.5).filled(-1)
data['AMP'] = hdu['EMLINE_GA'].data[el['Ha-6564']].ravel()[uniq_indx]
data['ANR'] = hdu['EMLINE_GANR'].data[el['Ha-6564']].ravel()[uniq_indx]
data['RCHI2'] = hdu['EMLINE_LFOM'].data[el['Ha-6564']].ravel()[uniq_indx]
data['CNT'] = hdu['EMLINE_GEW_CNT'].data[el['Ha-6564']].ravel()[uniq_indx]
data['EW'] = hdu['EMLINE_GEW'].data[el['Ha-6564']].ravel()[uniq_indx]
data['EW_ERR'] = numpy.ma.power(
hdu['EMLINE_GEW_IVAR'].data[el['Ha-6564']].ravel()[uniq_indx],
-0.5).filled(-1)
data['VEL'] = hdu['EMLINE_GVEL'].data[el['Ha-6564']].ravel()[uniq_indx]
data['VEL_ERR'] = numpy.ma.power(
hdu['EMLINE_GVEL_IVAR'].data[el['Ha-6564']].ravel()[uniq_indx],
-0.5).filled(-1)
data['SIG'] = hdu['EMLINE_GSIGMA'].data[el['Ha-6564']].ravel()[uniq_indx]
data['SIG_ERR'] = numpy.ma.power(
hdu['EMLINE_GSIGMA_IVAR'].data[el['Ha-6564']].ravel()[uniq_indx],
-0.5).filled(-1)
data['SIG_INST'] = hdu['EMLINE_INSTSIGMA'].data[
el['Ha-6564']].ravel()[uniq_indx]
# Deal with the masking
qual_bm = DAPQualityBitMask()
data_bm = HalphaBitMask()
if qual_bm.flagged(hdu[0].header['DAPQUAL'], flag='CRITICAL'):
data['MASK'] = data_bm.turn_on(data['MASK'], 'CRITCUBE')
indx = hdu['EMLINE_GFLUX_MASK'].data[el['Ha-6564']].ravel()[uniq_indx] > 0
if numpy.any(indx):
data['MASK'][indx] = data_bm.turn_on(data['MASK'][indx], 'BADFLUX')
indx = hdu['EMLINE_GEW_MASK'].data[el['Ha-6564']].ravel()[uniq_indx] > 0
if numpy.any(indx):
data['MASK'][indx] = data_bm.turn_on(data['MASK'][indx], 'BADEW')
indx = (hdu['EMLINE_GVEL_MASK'].data.ravel()[uniq_indx] > 0) \
| numpy.logical_not(hdu['EMLINE_GVEL_IVAR'].data.ravel()[uniq_indx] > 0)
if numpy.any(indx):
data['MASK'][indx] = data_bm.turn_on(data['MASK'][indx], 'BADVEL')
indx = (hdu['EMLINE_GSIGMA_MASK'].data.ravel()[uniq_indx] > 0) \
| numpy.logical_not(hdu['EMLINE_GSIGMA_IVAR'].data.ravel()[uniq_indx] > 0)
if numpy.any(indx):
data['MASK'][indx] = data_bm.turn_on(data['MASK'][indx], 'BADSIG')
return data
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 include_maps_data(tbl, maps_file, exclude_ancillary=False):
qual_bm = DAPQualityBitMask()
bm = DAPMapsBitMask()
# # Targeting bits
# sdssMaskbits = defaults.sdss_maskbits_file()
# mngtarg1_bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
# mngtarg3_bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET3')
with fits.open(maps_file) as hdu:
# Check if the file was CRITICAL
if qual_bm.flagged(hdu['PRIMARY'].header['DAPQUAL'], flag='DRPCRIT'):
return 1
if exclude_ancillary and hdu[0].header['MNGTARG1'] == 0:
return 1
d4000_bins = numpy.unique(tbl['D4000BIN'])
sigma_bins = numpy.unique(tbl['SIGMABIN'])
haew_bins = numpy.unique(tbl['HAEWBIN'])
snr_bins = numpy.unique(tbl['SNRBIN'])
nsigma = len(sigma_bins) - 1
nd4000 = len(d4000_bins) - 1
nhaew = len(haew_bins) - 1
nsnr = len(snr_bins) - 1
el = channel_dictionary(hdu, 'EMLINE_GFLUX')
si = channel_dictionary(hdu, 'SPECINDEX')
plt, ifu = map(lambda x: int(x),
os.path.split(maps_file)[1].split('-')[1:3])
# Input redshift
z = hdu['PRIMARY'].header['SCINPVEL'] / astropy.constants.c.to(
'km/s').value
# Stellar-kinematics binning map
binid, bin_indx = map(
lambda x: x[1:],
numpy.unique(hdu['BINID'].data[1, :, :].ravel(),
return_index=True))
# Get the D4000, H-alpha EW, and sigma values with S/N greater
# than one and unmasked
good_snr = hdu['SPX_SNR'].data.ravel() > 1
good_haew = numpy.logical_not(
bm.flagged(hdu['EMLINE_GEW_MASK'].data[el['Ha-6564']].ravel(),
flag='DONOTUSE'))
good_d4000 = numpy.logical_not(
bm.flagged(hdu['SPECINDEX_MASK'].data[si['D4000']].ravel(),
flag='DONOTUSE'))
good_sigma = numpy.logical_not(
bm.flagged(hdu['STELLAR_SIGMA_MASK'].data.ravel(),
flag='DONOTUSE'))
indx = (good_snr & good_haew & good_d4000 & good_sigma)[bin_indx]
snr = hdu['SPX_SNR'].data.ravel()[bin_indx][indx]
stvel = hdu['STELLAR_VEL'].data.ravel()[bin_indx][indx]
sigma = hdu['STELLAR_SIGMA'].data.ravel()[bin_indx][indx]
haanr = hdu['EMLINE_GANR'].data[el['Ha-6564']].ravel()[bin_indx][indx]
havel = hdu['EMLINE_GVEL'].data[el['Ha-6564']].ravel()[bin_indx][indx]
hasig = hdu['EMLINE_GSIGMA'].data[
el['Ha-6564']].ravel()[bin_indx][indx]
haew = hdu['EMLINE_GEW'].data[el['Ha-6564']].ravel()[bin_indx][indx]
d4000 = hdu['SPECINDEX'].data[si['D4000']].ravel()[bin_indx][indx]
binid = binid[indx]
d4000_i = numpy.digitize(d4000, d4000_bins[1:-1])
sigma_j = numpy.digitize(sigma, sigma_bins[1:-1])
haew_k = numpy.digitize(haew, haew_bins[1:-1])
snr_l = numpy.digitize(snr, snr_bins[1:-1])
for i in range(nd4000):
dindx = d4000_i == i
for j in range(nsigma):
sindx = sigma_j == j
for k in range(nhaew):
hindx = haew_k == k
for l in range(nsnr):
nindx = snr_l == l
indx = dindx & sindx & hindx & nindx
nbin = numpy.sum(indx)
if nbin == 0:
continue
ii = i * nsigma * nhaew * nsnr + j * nhaew * nsnr + k * nsnr + l
tbl['NBIN'][ii] += nbin
_snr = snr[indx]
# Still find the highest S/N spectrum in each
# bin
m = numpy.argsort(_snr)[-1]
if _snr[m] < tbl['SNR'][ii]:
continue
tbl['PLT'][ii] = plt
tbl['IFU'][ii] = ifu
tbl['BIN'][ii] = binid[indx][m]
tbl['MNGTARG1'][ii] = int(hdu[0].header['MNGTARG1'])
tbl['MNGTARG3'][ii] = int(hdu[0].header['MNGTARG3'])
tbl['Z'][ii] = z
tbl['SNR'][ii] = snr[indx][m]
tbl['STZ'][ii] \
= stvel[indx][m]*(1+z)/astropy.constants.c.to('km/s').value + z
tbl['SIGMA'][ii] = sigma[indx][m]
tbl['HAANR'][ii] = haanr[indx][m]
tbl['HAZ'][ii] \
= havel[indx][m]*(1+z)/astropy.constants.c.to('km/s').value + z
tbl['HASIGMA'][ii] = hasig[indx][m]
tbl['HAEW'][ii] = haew[indx][m]
tbl['D4000'][ii] = d4000[indx][m]
return 0
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)