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])