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_grid_borders():
x = numpy.linspace(-1, 1, 100)
dx = x[1] - x[0]
nx, rng = sampling.grid_npix(rng=[x[0], x[-1]], dx=dx)
borders, _dx = sampling.grid_borders(rng, nx)
assert dx == _dx, 'Bad linear step size'
assert numpy.allclose(borders, numpy.linspace(-1 - dx / 2, 1 + dx / 2,
101)), 'Bad linear borders'
x = numpy.logspace(-1, 1, 100)
dx = numpy.log10(x[1]) - numpy.log10(x[0])
nx, rng = sampling.grid_npix(rng=[x[0], x[-1]], dx=dx, log=True)
borders, _dx = sampling.grid_borders(rng, nx, log=True)
assert dx == _dx, 'Bad logarithmic step size'
assert numpy.allclose(borders,
numpy.logspace(-1 - dx / 2, 1 + dx / 2,
101)), 'Bad geometric borders'
def resample_test():
# Read the example datacube and get the expected redshift You can download
# these data using
# https://github.com/sdss/mangadap/blob/master/download_test_data.py
plt = 7815
ifu = 3702
drpver = 'v3_1_1'
directory_path = defaults.dap_source_dir() / 'data' / 'remote'
cube = MaNGADataCube.from_plateifu(plt, ifu, directory_path=directory_path)
drpall_file = directory_path / f'drpall-{drpver}.fits'
z = get_redshift(plt, ifu, drpall_file)
# Pull out two example spectra from the datacube
old_wave = cube.wave
old_flux = numpy.ma.MaskedArray(cube.flux[10, 10:12, :],
mask=cube.mask[10, 10:12, :] > 0)
old_flux[:, (old_wave > 5570) & (old_wave < 5586)] = numpy.ma.masked
old_ferr = numpy.ma.power(cube.ivar[10, 10:12, :], -0.5)
if spectres is not None:
# Use spectres to resample the spectrum, ignoring last pixel
indx = (old_wave > old_wave[0] / (1 + z)) & (old_wave < old_wave[-2] /
(1 + z))
t = time.perf_counter()
new_flux_spectres = numpy.empty((old_flux.shape[0], numpy.sum(indx)),
dtype=float)
new_ferr_spectres = numpy.empty((old_flux.shape[0], numpy.sum(indx)),
dtype=float)
for i in range(old_flux.shape[0]):
new_flux_spectres[i,:], new_ferr_spectres[i,:] \
= spectres.spectres(old_wave[indx], old_wave/(1+z), old_flux[i,:].filled(0.0),
spec_errs=old_ferr[i,:].filled(0.0))
print('SpectRes Time: ', time.perf_counter() - t)
# Use a brute-force integration of the spectra to resample the spectrum
t = time.perf_counter()
borders = 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 = numpy.array([
passband_integral(old_wave / (1 + z), f, passband=_p, log=True)
for f in old_flux.filled(0.0)
])
new_flux_brute /= (_p[:, 1] - _p[:, 0])[None, :]
print('Brute Force Time: ', time.perf_counter() - t)
# Use the Resample class to resample the spectrum
t = time.perf_counter()
r = Resample(old_flux,
e=old_ferr,
x=old_wave / (1 + z),
newRange=[old_wave[0], old_wave[-1]],
inLog=True,
newLog=True)
print('Resample Time: ', time.perf_counter() - t)
# Estimate the differences between the resampling methods (these should all
# be the same to nearly numerical accuracy)
print('Mean diff:')
if spectres is not None:
print(' spectres - brute = {0:.5e}'.format(
numpy.mean(
numpy.absolute(new_flux_spectres - new_flux_brute[:, indx]))))
print(' spectres - resample = {0:.5e}'.format(
numpy.mean(numpy.absolute(new_flux_spectres - r.outy[:, indx]))))
print(' brute - resample = {0:.5e}'.format(
numpy.mean(numpy.absolute(new_flux_brute - r.outy))))
# Plot the original and resampled versions for all spectra. The resampled
# versions should all be indistinguishable.
for i in range(old_flux.shape[0]):
pyplot.plot(old_wave / (1 + z), old_flux[i, :], label='Data')
if spectres is not None:
pyplot.plot(old_wave[indx],
new_flux_spectres[i, :],
label='spectres')
pyplot.plot(old_wave, new_flux_brute[i, :], label='brute')
pyplot.plot(r.outx, r.outy[i, :], label='Resample')
pyplot.plot(r.outx, r.outf[i, :], label='Good-pixel Mask')
pyplot.legend()
pyplot.xlabel('Wavelength')
pyplot.ylabel('Flux')
pyplot.show()