def test_coord_transform(self):
"""WaveCoord class: testing coordinates transformations"""
wave = WaveCoord(crval=0, cunit=u.nm, shape=10)
pixel = wave.pixel(wave.coord(5, unit=u.nm), nearest=True, unit=u.nm)
assert pixel == 5
wave2 = np.arange(10)
pixel = wave.pixel(wave.coord(wave2, unit=u.nm), nearest=True,
unit=u.nm)
assert_array_equal(pixel, wave2)
pix = np.arange(wave.shape, dtype=float)
np.testing.assert_allclose(wave.pixel(wave.coord(unit=u.nm),
unit=u.nm), pix)
def test_rebin(self):
"""WCS class: testing rebin method"""
wave = WaveCoord(crval=0, cunit=u.nm, shape=10)
wave.rebin(factor=2)
assert wave.get_step(unit=u.nm) == 2.0
assert wave.get_start(unit=u.nm) == 0.5
assert wave.coord(2, unit=u.nm) == 4.5
assert wave.shape == 5
def test_coord(self):
"""WaveCoord class: testing getting the coordinates"""
wave = WaveCoord(crval=1, cunit=u.nm, shape=10)
# By default the CTYPE is LINEAR and can't be converted to air or
# vacuum.
with pytest.raises(ValueError):
wave.coord(medium='air')
with pytest.raises(ValueError):
wave.coord(medium='vacuum')
wave.wcs.wcs.ctype = ['WAVE']
with pytest.raises(ValueError):
# Unknown parameter value
wave.coord(medium='vacuu')
refcoord = np.arange(10) + 1
assert_array_equal(wave.coord(medium='vacuum'), refcoord)
assert_array_equal(wave.coord(medium='air'), vactoair(refcoord))
wave.wcs.wcs.ctype = ['AWAV']
assert_array_equal(wave.coord(medium='air'), refcoord)
assert_array_equal(wave.coord(medium='vacuum'), airtovac(refcoord))
def test_resample():
"""Spectrum class: Test resampling"""
# Choose the dimensions of the spectrum, choosing a large number that is
# *not* a convenient power of 2.
oldshape = 4000
# Choose the wavelength pixel size and the default wavelength units.
oldstep = 1.0
oldunit = u.angstrom
# Create the wavelength axis coordinates.
wave = WaveCoord(crpix=2.0,
cdelt=oldstep,
crval=0.5,
cunit=oldunit,
shape=oldshape)
# Specify the desired increase in pixel size, and the resulting pixel size.
factor = 6.5
newstep = ((factor * oldstep) * oldunit).to(u.nm).value
# Specify the wavelength at which the peak of the resampled spectrum should
# be expected.
expected_peak_wave = 3000.0
# Create the array in which the test spectrum will be composed.
data = np.zeros(oldshape)
# Get the wavelength coordinates of each pixel in the spectrum.
w = wave.coord()
# Add the following list gaussians to the spectrum, where each
# gaussian is specified as: (amplitude, sigma_in_pixels,
# center_wavelength). Given that narrow gaussians are reduced in
# amplitude by resampling more than wide gaussians, we arrange
# that the peak gaussian before and after correctly resampling are
# different.
gaussians = [
(0.5, 12.0, 800.0),
(0.7, 5.0, 1200.0),
(0.4, 700.0, 1600.0),
(1.5, 2.6, 1980.0), # Peak before resampling
(1.2, 2.6, 2000.0),
(1.3, 15.0, expected_peak_wave), # Peak if resampled correctly
(1.0, 2.0, 3200.0)
]
for amp, sigma, center in gaussians:
sigma *= oldstep
data += amp * np.exp(-0.5 * ((center - w) / sigma)**2)
# Fill the variance array with a simple window function.
var = np.hamming(oldshape)
# Add gaussian random noise to the spectrum, but leave 3 output
# pixel widths zero at each end of the spectrum so that the PSF of
# the output grid doesn't spread flux from the edges off the edge
# of the output grid. It takes about 3 pixel widths for the gaussian
# PSF to drop to about 0.01 of its peak.
margin = np.ceil(3 * factor).astype(int)
data[margin:-margin] += np.random.normal(scale=0.1,
size=data.shape - 2 * margin)
# Install the spectral data in a Spectrum container.
oldsp = Spectrum(data=data, var=var, wave=wave)
# Mask a few pixels.
masked_slice = slice(900, 910)
oldsp.mask[masked_slice] = True
# Create a down-sampled version of the input spectrum.
newsp = oldsp.resample(newstep, unit=u.nm)
# Check that the integral flux in the resampled spectrum matches that of
# the original spectrum.
expected_flux = oldsp.sum(weight=False)[0] * oldsp.wave.get_step(
unit=oldunit)
actual_flux = newsp.sum(weight=False)[0] * newsp.wave.get_step(
unit=oldunit)
assert_allclose(actual_flux, expected_flux, 1e-2)
# Do the same test, but with fluxes weighted by the inverse of the variances.
expected_flux = oldsp.sum(weight=True)[0] * oldsp.wave.get_step(
unit=oldunit)
actual_flux = newsp.sum(weight=True)[0] * newsp.wave.get_step(unit=oldunit)
assert_allclose(actual_flux, expected_flux, 1e-2)
# Check that the peak of the resampled spectrum is at the wavelength
# where the strongest gaussian was centered in the input spectrum.
assert_allclose(np.argmax(newsp.data),
newsp.wave.pixel(expected_peak_wave, nearest=True))
# Now upsample the downsampled spectrum to the original pixel size.
# This won't recover the same spectrum, since higher spatial frequencies
# are lost when downsampling, but the total flux should be about the
# same, and the peak should be at the same wavelength as the peak in
# original spectrum within one pixel width of the downsampled spectrum.
newsp2 = newsp.resample(oldstep, unit=oldunit)
# Check that the doubly resampled spectrum has the same integrated flux
# as the original.
expected_flux = oldsp.sum(weight=False)[0] * oldsp.wave.get_step(
unit=oldunit)
actual_flux = newsp2.sum(weight=False)[0] * newsp2.wave.get_step(
unit=oldunit)
assert_allclose(actual_flux, expected_flux, 1e-2)
# Check that the peak of the up-sampled spectrum is at the wavelength
# of the peak of the down-sampled spectrum to within the pixel resolution
# of the downsampled spectrum.
assert_allclose(
newsp.wave.pixel(newsp2.wave.coord(np.argmax(newsp2.data)),
nearest=True),
newsp.wave.pixel(expected_peak_wave, nearest=True))
# Check that pixels that were masked in the input spectrum are still
# masked in the final spectrum.
np.testing.assert_equal(newsp2.mask[masked_slice],
oldsp.mask[masked_slice])