def test_rukia_fit():
miles_wave, miles_flux = read_miles(
data_test_file('MILES_res2.50_star_m0505V.fits'))
miles_flux /= numpy.median(miles_flux)
xsl_wave, xsl_flux, _ = read_xsl(
data_test_file('xsl_spectrum_X0360_uvb.fits'))
xsl_flux /= numpy.ma.median(xsl_flux)
# Setup the fitting functions
sigma = LegendrePolynomial(1)
mulp = LegendrePolynomial(5)
r = Rukia(sigma, mul_model=mulp)
# Perform the fit
r.fit(miles_wave,
miles_flux,
xsl_wave.data,
xsl_flux.data,
shift=0.0,
fit_shift=True,
rejiter=-1)
assert numpy.sum(r.gpm) == 2176, 'Change in the number of pixels rejected.'
assert numpy.sqrt(numpy.mean(numpy.square((r.flux-r.model(r.par))[r.gpm]))) < 0.01, \
'Fit quality changed.'
def test_manga_dap():
odir = data_test_file('manga_dap_output')
# Clean up previous failure
if os.path.isdir(odir):
shutil.rmtree(odir)
# Run the DAP. The binning in plan.par is set to ALL binning, so
# this run of the DAP just analyzes one spectrum and takes about a
# minute.
manga_dap.main(
manga_dap.parse_args([
'-c',
data_test_file('datacube.ini'), '-p',
data_test_file('plan.par'), '-a', odir
]))
# Re-run to use existing files. Takes about 40s.
manga_dap.main(
manga_dap.parse_args([
'-c',
data_test_file('datacube.ini'), '-p',
data_test_file('plan.par'), '-a', odir
]))
# Clean up
shutil.rmtree(odir)
def test_manga_dap_import():
with pytest.raises(ImportError):
manga_dap.main(
manga_dap.parse_args(
['-c', data_test_file('datacube.ini'), '-m', 'junk']))
with pytest.raises(AttributeError):
manga_dap.main(
manga_dap.parse_args(
['-c', data_test_file('datacube.ini'), '-o', 'junk']))
def test_write():
directory, ofile = TemplateLibrary.default_paths(
'MILESHC', output_path=data_test_file())
file_name = directory / ofile
if file_name.exists():
os.remove(str(file_name))
tpl = TemplateLibrary('MILESHC',
match_resolution=False,
velscale_ratio=4,
spectral_step=1e-4,
log=True,
output_path=data_test_file(),
hardcopy=True)
assert file_name.exists(), 'File not written'
os.remove(str(file_name))
def test_against_brute_force():
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
# Just test on the first spectrum
old_wave = hdu['WAVE'].data
old_flux = numpy.ma.MaskedArray(hdu['FLUX'].data[0, :],
mask=hdu['MASK'].data[0, :] > 0)
old_flux[(old_wave > 5570) & (old_wave < 5586)] = numpy.ma.masked
old_ferr = numpy.ma.power(hdu['IVAR'].data[0, :], -0.5)
z = hdu['Z'].data[0]
# Do the brute force calculation
borders = sampling.grid_borders(numpy.array([old_wave[0], old_wave[-1]]),
old_wave.size,
log=True)[0]
_p = numpy.repeat(borders, 2)[1:-1].reshape(-1, 2)
new_flux_brute = passband_integral(old_wave / (1 + z),
old_flux,
passband=_p,
log=True)
new_flux_brute /= (_p[:, 1] - _p[:, 0])
# Use resample
r = sampling.Resample(old_flux,
e=old_ferr,
x=old_wave / (1 + z),
newRange=[old_wave[0], old_wave[-1]],
inLog=True,
newLog=True)
# The two shoule be the same
assert numpy.isclose(numpy.mean(numpy.absolute(new_flux_brute-r.outy)), 0.0), \
'Resampling and brute force calculation should be identical'
def test_pixelmask():
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
pixelmask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPSCMSK'))
assert numpy.sum(pixelmask.boolean(hdu['WAVE'].data, nspec=1)) == 489, \
'Incorrect number of masked pixels'
def test_drpbitmask():
# Read the data
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
drpbm = DRPFitsBitMask()
assert numpy.sum(drpbm.flagged(hdu['MASK'].data, MaNGADataCube.do_not_fit_flags())) == 4601, \
'Flags changed'
def test_multi_read():
plan = AnalysisPlan.from_toml(data_test_file('dr17.toml'))
assert len(plan.keys()) == 4, 'Number of example plans changed'
assert plan['plan3'][
'key'] == 'HYB10-MILESHC-MASTARSSP', 'Plan key changed'
assert plan.binning['plan1']['key'] == 'SPX', 'Binning changed'
assert plan.elfit['plan4'][
'key'] == 'EFITHC2DB', 'Emission-line fitting key changed'
def test_read():
plan = AnalysisPlan.from_toml(data_test_file('global_bin.toml'))
assert len(plan.keys()) == 1, 'Number of example plans changed'
assert list(plan.keys())[0] == 'default', 'Name changed'
assert plan.binning['default'][
'key'] == 'ALL', 'Default DRP reduction QA key changed.'
assert plan.elfit['default'][
'key'] == 'EFITSSP', 'Default emission-line fit key changed.'
def test_from_config():
cfg = MaNGAConfig.from_config(data_test_file("datacube.ini"))
assert isinstance(cfg.directory_path,
Path), 'Directory should be a Path instance'
# TODO: This passes locally, but will fail the tox CI tests. Determine how
# to perform this in tox?
#assert cfg.directory_path == Path(remote_data_file()).resolve(), 'Directory path changed'
assert cfg.plate == 7815, 'Plate changed'
assert cfg.log, 'Log binning should be true'
assert cfg.mode == 'CUBE', 'Should be in CUBE mode'
assert cfg.file_name == 'manga-7815-3702-LOGCUBE.fits.gz'
def test_mangaplan():
cfg = MaNGAConfig(7815, 3702)
plan = MaNGAAnalysisPlan.from_toml(data_test_file('dr17.toml'), cube=cfg)
assert plan.dap_file_root(cfg) == 'manga-7815-3702', 'Bad root'
assert plan.dap_file_root(cfg, mode='MAPS', plan_index=0) \
== 'manga-7815-3702-MAPS-SPX-MILESHC-MASTARSSP', 'Bad full root'
assert plan.common_path().parts[-2:] == (str(cfg.plate), str(cfg.ifudesign)), \
'Bad common subdirectories'
assert plan.method_path().parts[-2:] == (str(cfg.plate), str(cfg.ifudesign)), \
'Bad method subdirectories'
assert plan.method_path(qa=True).parts[-2:] == (str(cfg.ifudesign), 'qa'), \
'Bad qa subdirectories'
def test_moments_with_continuum():
# Read the data
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
drpbm = DRPFitsBitMask()
flux = numpy.ma.MaskedArray(hdu['FLUX'].data,
mask=drpbm.flagged(
hdu['MASK'].data,
MaNGADataCube.do_not_fit_flags()))
ferr = numpy.ma.power(hdu['IVAR'].data, -0.5)
flux[ferr.mask] = numpy.ma.masked
ferr[flux.mask] = numpy.ma.masked
nspec = flux.shape[0]
# Instantiate the template libary
velscale_ratio = 4
tpl = TemplateLibrary('MILESHC',
match_resolution=False,
velscale_ratio=velscale_ratio,
spectral_step=1e-4,
log=True,
hardcopy=False)
tpl_sres = numpy.mean(tpl['SPECRES'].data, axis=0)
# Get the pixel mask
pixelmask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPSCMSK'))
# Instantiate the fitting class
ppxf = PPXFFit(StellarContinuumModelBitMask())
# Perform the fit
fit_wave, fit_flux, fit_mask, fit_par \
= ppxf.fit(tpl['WAVE'].data.copy(), tpl['FLUX'].data.copy(), hdu['WAVE'].data, flux, ferr,
hdu['Z'].data, numpy.full(nspec, 100.), iteration_mode='no_global_wrej',
reject_boxcar=100, ensemble=False, velscale_ratio=velscale_ratio,
mask=pixelmask, matched_resolution=False, tpl_sres=tpl_sres,
obj_sres=hdu['SRES'].data, degree=8, moments=2)
# Remask the continuum fit
sc_continuum = StellarContinuumModel.reset_continuum_mask_window(
numpy.ma.MaskedArray(fit_flux, mask=fit_mask > 0))
# Read the database that define the emission lines and passbands
momdb = EmissionMomentsDB.from_key('ELBMILES')
# Measure the moments
elmombm = EmissionLineMomentsBitMask()
elmom = EmissionLineMoments.measure_moments(momdb,
hdu['WAVE'].data,
flux,
continuum=sc_continuum,
redshift=hdu['Z'].data,
bitmask=elmombm)
# Measure the EW based on the moments
include_band = numpy.array([numpy.invert(momdb.dummy)]*nspec) \
& numpy.invert(elmombm.flagged(elmom['MASK'],
flag=['BLUE_EMPTY', 'RED_EMPTY']))
line_center = (1.0 + hdu['Z'].data)[:, None] * momdb['restwave'][None, :]
elmom['BMED'], elmom['RMED'], pos, elmom['EWCONT'], elmom['EW'], elmom['EWERR'] \
= emission_line_equivalent_width(hdu['WAVE'].data, flux, momdb['blueside'],
momdb['redside'], line_center, elmom['FLUX'],
redshift=hdu['Z'].data,
line_flux_err=elmom['FLUXERR'],
include_band=include_band)
# Check the flags
reference = {
'BLUE_INCOMP': 21,
'MAIN_JUMP': 0,
'UNDEFINED_MOM2': 42,
'JUMP_BTWN_SIDEBANDS': 0,
'RED_JUMP': 0,
'DIVBYZERO': 0,
'NO_ABSORPTION_CORRECTION': 0,
'RED_EMPTY': 21,
'UNDEFINED_BANDS': 8,
'DIDNOTUSE': 0,
'UNDEFINED_MOM1': 0,
'FORESTAR': 0,
'NON_POSITIVE_CONTINUUM': 0,
'LOW_SNR': 0,
'MAIN_EMPTY': 21,
'BLUE_JUMP': 0,
'RED_INCOMP': 21,
'MAIN_INCOMP': 21,
'BLUE_EMPTY': 21
}
assert numpy.all([
reference[k] == numpy.sum(elmombm.flagged(elmom['MASK'], flag=k))
for k in elmombm.keys()
]), 'Number of flagged measurements changed'
# Check that the values are finite
assert numpy.all([ numpy.all(numpy.isfinite(elmom[n])) for n in elmom.dtype.names]), \
'Found non-finite values in output'
# Check the band definitions
assert numpy.all(numpy.equal(elmom['REDSHIFT'],
hdu['Z'].data)), 'Redshift changed'
assert numpy.all(numpy.isclose(numpy.mean(momdb['blueside'], axis=1)[None,:],
elmom['BCEN']/(1+hdu['Z'].data[:,None]))
| elmombm.flagged(elmom['MASK'], flag='UNDEFINED_BANDS')), \
'Blue passband center incorrect'
assert numpy.all(numpy.isclose(numpy.mean(momdb['redside'], axis=1)[None,:],
elmom['RCEN']/(1+hdu['Z'].data[:,None]))
| elmombm.flagged(elmom['MASK'], flag='UNDEFINED_BANDS')), \
'Red passband center incorrect'
# Check the values
assert numpy.all(
numpy.absolute(elmom['FLUX'][0] - numpy.array([
0.63, 0.00, 0.22, -1.32, -0.88, -0.68, -0.44, -0.13, -1.14, -0.07,
-0.11, 0.01, 0.38, 0.73, 0.71, 0.44, 0.08, 0.74, 1.30, 2.34, 0.55,
0.44
])) < 0.01), 'Fluxes too different'
assert numpy.all(numpy.absolute(elmom['MOM1'][0] -
numpy.array([ 14682.6, 0.0, 14843.2, 14865.8, 14890.4, 14404.7,
14208.6, 12376.0, 14662.5, 14148.5, 15804.1, 17948.4,
14874.5, 14774.9, 14840.5, 14746.0, 15093.1, 14857.8,
14839.0, 14840.2, 14876.0, 14859.5])) < 0.1), \
'1st moments too different'
assert numpy.all(numpy.absolute(elmom['MOM2'][0] -
numpy.array([322.2, 0.0, 591.4, 436.4, 474.6, 0.0, 0.0, 0.0,
364.6, 0.0, 0.0, 0.0, 289.1, 226.9, 282.6, 283.8,
227.0, 207.7, 207.7, 253.6, 197.0, 212.4])) < 0.1), \
'2nd moments too different'
assert numpy.all(numpy.absolute(elmom['EW'][0] -
numpy.array([ 0.63, 0.00, 0.20, -1.28, -0.76, -0.54, -0.30, -0.09,
-0.61, -0.03, -0.04, 0.00, 0.13, 0.25, 0.24, 0.13,
0.02, 0.22, 0.38, 0.69, 0.17, 0.13])) < 0.01), \
'EW too different'
def test_sasuke():
# Read the data
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
drpbm = DRPFitsBitMask()
flux = numpy.ma.MaskedArray(hdu['FLUX'].data,
mask=drpbm.flagged(
hdu['MASK'].data,
MaNGADataCube.do_not_fit_flags()))
ferr = numpy.ma.power(hdu['IVAR'].data, -0.5)
flux[ferr.mask] = numpy.ma.masked
ferr[flux.mask] = numpy.ma.masked
nspec = flux.shape[0]
# Instantiate the template libary
velscale_ratio = 4
tpl = TemplateLibrary('MILESHC',
match_resolution=False,
velscale_ratio=velscale_ratio,
spectral_step=1e-4,
log=True,
hardcopy=False)
tpl_sres = numpy.mean(tpl['SPECRES'].data, axis=0)
# Get the pixel mask
pixelmask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPSCMSK'))
# Instantiate the fitting class
ppxf = PPXFFit(StellarContinuumModelBitMask())
# Perform the fit
sc_wave, sc_flux, sc_mask, sc_par \
= ppxf.fit(tpl['WAVE'].data.copy(), tpl['FLUX'].data.copy(), hdu['WAVE'].data, flux, ferr,
hdu['Z'].data, numpy.full(nspec, 100.), iteration_mode='no_global_wrej',
reject_boxcar=100, ensemble=False, velscale_ratio=velscale_ratio,
mask=pixelmask, matched_resolution=False, tpl_sres=tpl_sres,
obj_sres=hdu['SRES'].data, degree=8, moments=2)
# Mask the 5577 sky line
pixelmask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'))
# Read the emission line fitting database
emldb = EmissionLineDB.from_key('ELPMILES')
assert emldb['name'][
18] == 'Ha', 'Emission-line database names or ordering changed'
# Instantiate the fitting class
emlfit = Sasuke(EmissionLineModelBitMask())
# Perform the fit
el_wave, model, el_flux, el_mask, el_fit, el_par \
= emlfit.fit(emldb, hdu['WAVE'].data, flux, obj_ferr=ferr, obj_mask=pixelmask,
obj_sres=hdu['SRES'].data, guess_redshift=hdu['Z'].data,
guess_dispersion=numpy.full(nspec, 100.), reject_boxcar=101,
stpl_wave=tpl['WAVE'].data, stpl_flux=tpl['FLUX'].data,
stpl_sres=tpl_sres, stellar_kinematics=sc_par['KIN'],
etpl_sinst_mode='offset', etpl_sinst_min=10.,
velscale_ratio=velscale_ratio, matched_resolution=False)
# Rejected pixels
assert numpy.sum(emlfit.bitmask.flagged(el_mask, flag='PPXF_REJECT')) == 266, \
'Different number of rejected pixels'
# Unable to fit
assert numpy.array_equal(emlfit.bitmask.flagged_bits(el_fit['MASK'][5]), ['NO_FIT']), \
'Expected NO_FIT in 6th spectrum'
# No *attempted* fits should fail
assert numpy.sum(emlfit.bitmask.flagged(el_fit['MASK'], flag='FIT_FAILED')) == 0, \
'Fits should not fail'
# Number of used templates
assert numpy.array_equal(numpy.sum(numpy.absolute(el_fit['TPLWGT']) > 1e-10, axis=1),
[25, 22, 34, 32, 27, 0, 16, 22]), \
'Different number of templates with non-zero weights'
# No additive coefficients
assert numpy.all(el_fit['ADDCOEF'] == 0), \
'No additive coefficients should exist'
# No multiplicative coefficients
assert numpy.all(el_fit['MULTCOEF'] == 0), \
'No multiplicative coefficients should exist'
# Fit statistics
assert numpy.all(
numpy.absolute(
el_fit['RCHI2'] -
numpy.array([2.34, 1.22, 1.58, 1.88, 3.20, 0., 1.05, 0.88])) < 0.02
), 'Reduced chi-square are too different'
assert numpy.all(
numpy.absolute(el_fit['RMS'] - numpy.array(
[0.036, 0.019, 0.036, 0.024, 0.051, 0.000, 0.012, 0.012])) < 0.001
), 'RMS too different'
assert numpy.all(numpy.absolute(el_fit['FRMS'] -
numpy.array([0.021, 0.025, 0.025, 0.033, 0.018, 0.000,
1.052, 0.101])) < 0.001), \
'Fractional RMS too different'
assert numpy.all(numpy.absolute(el_fit['RMSGRW'][:,2] -
numpy.array([0.070, 0.038, 0.071, 0.047, 0.101, 0.000, 0.026,
0.024])) < 0.001), \
'Median absolute residual too different'
# All lines should have the same velocity
assert numpy.all(numpy.all(el_par['KIN'][:,:,0] == el_par['KIN'][:,None,0,0], axis=1)), \
'All velocities should be the same'
# Test velocity values
# TODO: Need some better examples!
assert numpy.all(numpy.absolute(el_par['KIN'][:,0,0] -
numpy.array([14704.9, 14869.3, 14767.1, 8161.9, 9258.7, 0.0,
5130.9, 5430.3])) < 0.1), \
'Velocities are too different'
# H-alpha dispersions
assert numpy.all(numpy.absolute(el_par['KIN'][:,18,1] -
numpy.array([1000.5, 1000.5, 224.7, 124.9, 171.2, 0.0, 81.2,
50.0])) < 1e-1), \
'H-alpha dispersions are too different'
def test_moments():
# Read the data
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
drpbm = DRPFitsBitMask()
flux = numpy.ma.MaskedArray(hdu['FLUX'].data,
mask=drpbm.flagged(
hdu['MASK'].data,
MaNGADataCube.do_not_fit_flags()))
ferr = numpy.ma.power(hdu['IVAR'].data, -0.5)
flux[ferr.mask] = numpy.ma.masked
ferr[flux.mask] = numpy.ma.masked
nspec = flux.shape[0]
# Read the database that define the emission lines and passbands
momdb = EmissionMomentsDB.from_key('ELBMILES')
# Measure the moments
elmombm = EmissionLineMomentsBitMask()
elmom = EmissionLineMoments.measure_moments(momdb,
hdu['WAVE'].data,
flux,
redshift=hdu['Z'].data,
bitmask=elmombm)
# Measure the EW based on the moments
include_band = numpy.array([numpy.invert(momdb.dummy)]*nspec) \
& numpy.invert(elmombm.flagged(elmom['MASK'],
flag=['BLUE_EMPTY', 'RED_EMPTY']))
line_center = (1.0 + hdu['Z'].data)[:, None] * momdb['restwave'][None, :]
elmom['BMED'], elmom['RMED'], pos, elmom['EWCONT'], elmom['EW'], elmom['EWERR'] \
= emission_line_equivalent_width(hdu['WAVE'].data, flux, momdb['blueside'],
momdb['redside'], line_center, elmom['FLUX'],
redshift=hdu['Z'].data,
line_flux_err=elmom['FLUXERR'],
include_band=include_band)
# Check the flags
reference = {
'BLUE_INCOMP': 21,
'MAIN_JUMP': 0,
'UNDEFINED_MOM2': 46,
'JUMP_BTWN_SIDEBANDS': 0,
'RED_JUMP': 0,
'DIVBYZERO': 0,
'NO_ABSORPTION_CORRECTION': 176,
'RED_EMPTY': 21,
'UNDEFINED_BANDS': 8,
'DIDNOTUSE': 0,
'UNDEFINED_MOM1': 0,
'FORESTAR': 0,
'NON_POSITIVE_CONTINUUM': 0,
'LOW_SNR': 0,
'MAIN_EMPTY': 21,
'BLUE_JUMP': 0,
'RED_INCOMP': 21,
'MAIN_INCOMP': 21,
'BLUE_EMPTY': 21
}
assert numpy.all([
reference[k] == numpy.sum(elmombm.flagged(elmom['MASK'], flag=k))
for k in elmombm.keys()
]), 'Number of flagged measurements changed'
# Check that the values are finite
assert numpy.all([ numpy.all(numpy.isfinite(elmom[n])) for n in elmom.dtype.names]), \
'Found non-finite values in output'
# Check the band definitions
assert numpy.all(numpy.equal(elmom['REDSHIFT'],
hdu['Z'].data)), 'Redshift changed'
assert numpy.all(numpy.isclose(numpy.mean(momdb['blueside'], axis=1)[None,:],
elmom['BCEN']/(1+hdu['Z'].data[:,None]))
| elmombm.flagged(elmom['MASK'], flag='UNDEFINED_BANDS')), \
'Blue passband center incorrect'
assert numpy.all(numpy.isclose(numpy.mean(momdb['redside'], axis=1)[None,:],
elmom['RCEN']/(1+hdu['Z'].data[:,None]))
| elmombm.flagged(elmom['MASK'], flag='UNDEFINED_BANDS')), \
'Red passband center incorrect'
# Check the values
assert numpy.allclose(elmom['FLUX'][0],
numpy.array([
-0.83366296, 0., -0.7368989, -6.84760392,
-5.8392653, -3.84394899, -9.63158548,
-10.1459227, -1.86639944, 0.19851703, 0.04831539,
-5.58001859, 0.86652478, -1.3277138, 4.48556862,
0.12541773, -1.37675776, 1.14456948, -1.41808526,
2.48743805, -0.31254732, 0.04046428
]),
rtol=0.0,
atol=1e-2), 'Fluxes changed'
assert numpy.allclose(
elmom['MOM1'][0],
numpy.array([
15403.91870501, 0., 13866.58355013, 14816.45834376, 14861.90408263,
14545.21106265, 14929.76054479, 14774.62443577, 14943.56586856,
13010.07824437, 15933.25294444, 14918.25984067, 14425.53398781,
15207.53998774, 14803.71786274, 14160.66542001, 14720.66321017,
14706.89675211, 14880.91017052, 14901.49219165, 14880.79548007,
15615.43369812
]),
rtol=0.0,
atol=1e-1), '1st moments changed'
assert numpy.allclose(elmom['MOM2'][0],
numpy.array([
0., 0., 0., 439.76305578, 479.32501708,
325.96571646, 348.71402151, 362.29430475,
128.76827924, 0., 0., 322.61461489, 268.26542796,
27.14271982, 259.24977286, 0., 181.94055378,
129.62366078, 147.48288905, 225.76488299,
132.57819153, 0.
]),
rtol=0.0,
atol=1e-1), '2nd moments changed'
assert numpy.allclose(elmom['EW'][0],
numpy.array([
-0.83148156, 0., -0.67854382, -6.65583709,
-4.99844209, -3.06783667, -6.6506484,
-6.86724193, -0.99166185, 0.08843696, 0.01728948,
-1.81199184, 0.28592615, -0.46054113, 1.48650809,
0.03822714, -0.40850899, 0.33980593, -0.42043643,
0.73608197, -0.09406925, 0.01217937
]),
rtol=0.0,
atol=1e-2), 'EW changed'
def test_ppxffit():
# Read the data
specfile = data_test_file('MaNGA_test_spectra.fits.gz')
hdu = fits.open(specfile)
drpbm = DRPFitsBitMask()
flux = numpy.ma.MaskedArray(hdu['FLUX'].data,
mask=drpbm.flagged(
hdu['MASK'].data,
MaNGADataCube.do_not_fit_flags()))
ferr = numpy.ma.power(hdu['IVAR'].data, -0.5)
flux[ferr.mask] = numpy.ma.masked
ferr[flux.mask] = numpy.ma.masked
nspec = flux.shape[0]
# Instantiate the template libary
velscale_ratio = 4
tpl = TemplateLibrary('MILESHC',
match_resolution=False,
velscale_ratio=velscale_ratio,
spectral_step=1e-4,
log=True,
hardcopy=False)
tpl_sres = numpy.mean(tpl['SPECRES'].data, axis=0)
# Get the pixel mask
pixelmask = SpectralPixelMask(artdb=ArtifactDB.from_key('BADSKY'),
emldb=EmissionLineDB.from_key('ELPSCMSK'))
# Instantiate the fitting class
ppxf = PPXFFit(StellarContinuumModelBitMask())
# Perform the fit
fit_wave, fit_flux, fit_mask, fit_par \
= ppxf.fit(tpl['WAVE'].data.copy(), tpl['FLUX'].data.copy(), hdu['WAVE'].data, flux, ferr,
hdu['Z'].data, numpy.full(nspec, 100.), iteration_mode='no_global_wrej',
reject_boxcar=100, ensemble=False, velscale_ratio=velscale_ratio,
mask=pixelmask, matched_resolution=False, tpl_sres=tpl_sres,
obj_sres=hdu['SRES'].data, degree=8, moments=2)
# Test the results
# Rejected pixels
assert numpy.sum(ppxf.bitmask.flagged(fit_mask, flag='PPXF_REJECT')) == 119, \
'Different number of rejected pixels'
# Unable to fit
assert numpy.array_equal(ppxf.bitmask.flagged_bits(fit_par['MASK'][5]), ['NO_FIT']), \
'Expected NO_FIT in 6th spectrum'
# Number of used templates
assert numpy.array_equal(numpy.sum(numpy.absolute(fit_par['TPLWGT']) > 1e-10, axis=1),
[12, 13, 17, 15, 15, 0, 8, 12]), \
'Different number of templates with non-zero weights'
# Number of additive coefficients
assert fit_par['ADDCOEF'].shape[
1] == 9, 'Incorrect number of additive coefficients'
# No multiplicative coefficients
assert numpy.all(fit_par['MULTCOEF'] == 0), \
'No multiplicative coefficients should exist'
# Kinematics and errors
assert numpy.all(numpy.absolute(fit_par['KIN'] -
numpy.array([[ 14880.7, 292.9], [ 15053.4, 123.2],
[ 14787.5, 236.4], [ 8291.8, 169.7],
[ 9261.4, 202.7], [ 0.0, 0.0],
[ 5123.5, 63.8], [ 5455.6, 51.8]])) < 0.1), \
'Kinematics are too different'
assert numpy.all(numpy.absolute(fit_par['KINERR'] -
numpy.array([[2.0,1.9], [1.5,1.7], [ 2.4, 2.4], [2.2,2.3],
[1.1,1.1], [0.0,0.0], [26.1,30.8], [4.7,7.5]])) < 0.1), \
'Kinematic errors are too different'
# Velocity dispersion corrections
assert numpy.all(numpy.absolute(fit_par['SIGMACORR_SRES'] -
numpy.array([23.5, 10.1, 27.3, 38.7, 22.3, 0.0, 63.8, 23.8])) < 0.1), \
'SRES corrections are too different'
assert numpy.all(numpy.absolute(fit_par['SIGMACORR_EMP'] -
numpy.array([22.6, 0.0, 26.0, 38.2, 18.0, 0.0, 70.1, 0.0])) < 0.1), \
'EMP corrections are too different'
# Figures of merit
assert numpy.all(numpy.absolute(fit_par['RCHI2'] -
numpy.array([ 1.94, 1.18, 1.40, 1.53, 2.50, 0.00, 1.06, 0.86])) < 0.01), \
'Reduced chi-square too different'
assert numpy.all(
numpy.absolute(fit_par['RMS'] - numpy.array(
[0.033, 0.019, 0.034, 0.023, 0.046, 0.000, 0.015, 0.015])) < 0.001
), 'RMS too different'
assert numpy.all(
numpy.absolute(fit_par['FRMS'] - numpy.array(
[0.018, 0.023, 0.023, 0.032, 0.018, 0.000, 33.577, 0.148])) < 0.001
), 'Fractional RMS too different'
assert numpy.all(
numpy.absolute(fit_par['RMSGRW'][:, 2] - numpy.array(
[0.067, 0.037, 0.068, 0.046, 0.093, 0.000, 0.029, 0.027])) < 0.001
), 'Median absolute residual too different'
def test_from_config():
cube = MaNGADataCube.from_config(data_test_file('datacube.ini'))
assert cube.meta['z'] == 0.0293823, 'Bad config file read'
assert cube.meta['ell'] == 0.110844, 'Bad config file read'
def test_read():
plan = AnalysisPlanSet.from_par_file(data_test_file('plan.par'))
assert len(plan) == 1, 'Number of example plans changed'
assert plan[0]['bin_key'] == 'ALL', 'Binning changed'
assert plan[0]['elfit_key'] == 'EFITMPL11HC', 'Emission-line fitting key changed'
