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_resample():
delt = 100
x = numpy.arange(20) * 0.5 + delt
y = numpy.linspace(0.1, 2.0, 20)
y = y * y - y * y * y + 0.2 * y + 3 + 0.2 * numpy.square(numpy.sin(50 * y))
newRange = numpy.array([2, 8]) + delt
r = sampling.Resample(y,
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
step=False)
assert numpy.isclose(r.outx[0], newRange[0]), 'Incorrect starting value'
assert numpy.mean(interpolate.interp1d(x,y)(r.outx) - r.outy) < 1e-3, \
'Bad interpolation difference'
_r = sampling.Resample(y,
xRange=[x[0], x[-1]],
newBorders=r.outborders,
step=False)
assert numpy.array_equal(r.outx, _r.outx), 'Coordinates differ'
assert numpy.array_equal(r.outborders,
_r.outborders), 'Coordinates borders differ'
assert numpy.array_equal(r.outy, _r.outy), 'Resampling differs'
_r = sampling.Resample(y,
xRange=[x[0], x[-1]],
newx=r.outx,
newLog=True,
step=False)
assert numpy.array_equal(r.outx, _r.outx), 'Coordinates differ'
assert numpy.allclose(r.outborders, _r.outborders, rtol=0., atol=1e-12), \
'Coordinates borders differ'
assert numpy.allclose(r.outy, _r.outy, rtol=0., atol=1e-12), \
'Coordinates borders differ'
rc = sampling.Resample(y,
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
conserve=True,
step=False)
assert numpy.isclose(numpy.mean(r.outy/rc.outy), 3.250907334), \
'Flux conservation test failed'
def test_resample_covar():
delt = 100
x = numpy.arange(20) * 0.5 + delt
y = numpy.linspace(0.1, 2.0, 20)
y = y * y - y * y * y + 0.2 * y + 3 + 0.2 * numpy.square(numpy.sin(50 * y))
newRange = numpy.array([2, 8]) + delt
e = numpy.full_like(y, 2.)
r = sampling.Resample(y,
e=e,
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True)
# Test covar without errors
_r = sampling.Resample(y,
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
covar=True)
assert numpy.array_equal(r.outy, _r.outy), 'Bad resampling with covariance'
# Test multiple vectors
_r = sampling.Resample(numpy.row_stack((y, y)),
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
covar=True)
assert numpy.allclose(r.outy, _r.outy[0], rtol=0.0, atol=1e-15), \
'Bad resampling with covariance'
# Test covar with errors
_r = sampling.Resample(y,
xRange=[x[0], x[-1]],
e=e,
newRange=newRange,
newpix=40,
newLog=True,
covar=True)
assert numpy.array_equal(r.outy, _r.outy), 'Bad resampling with covariance'
assert numpy.array_equal(r.oute,
_r.oute), 'Bad resampling errors with covariance'
# Test multiple vectors with errors
_r = sampling.Resample(numpy.row_stack((y, y)),
e=numpy.row_stack((e, e)),
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
covar=True)
assert numpy.allclose(r.outy, _r.outy[0], rtol=0.0, atol=1e-15), \
'Bad resampling with covariance'
assert numpy.allclose(r.oute, _r.oute[0], rtol=0.0, atol=1e-15), \
'Bad resampling errors with covariance'
# Test with conserve
r = sampling.Resample(y,
e=e,
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
conserve=True)
_r = sampling.Resample(y,
xRange=[x[0], x[-1]],
e=e,
newRange=newRange,
newpix=40,
newLog=True,
covar=True,
conserve=True)
assert numpy.allclose(r.outy, _r.outy, rtol=0.0, atol=1e-15), \
'Bad resampling with covariance'
assert numpy.allclose(r.oute, _r.oute, rtol=0.0, atol=1e-15), \
'Bad resampling errors with covariance'
_r = sampling.Resample(numpy.row_stack((y, y)),
e=numpy.row_stack((e, e)),
xRange=[x[0], x[-1]],
newRange=newRange,
newpix=40,
newLog=True,
covar=True,
conserve=True)
assert numpy.allclose(r.outy, _r.outy[0], rtol=0.0, atol=1e-15), \
'Bad resampling with covariance'
assert numpy.allclose(r.oute, _r.oute[0], rtol=0.0, atol=1e-15), \
'Bad resampling errors with covariance'