def test_compare_specter(self):
ny, nx, nspec, nwave = self.A4.shape
psf = specter.psf.load_psf(self.psffile)
img = psf.project(self.wavelengths, self.phot, xyrange=self.xyrange)
# self.assertTrue(np.allclose(self.img, img))
#- Compare the "signal" decorrelation method
flux0, ivar0, R0 = specter.extract.ex2d_patch(self.noisyimg,
self.imgivar,
psf,
0,
nspec,
self.wavelengths,
xyrange=self.xyrange,
ndecorr=False)
flux1, ivar1, R1 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='signal')
#- Note that specter is using it's version of the projection matrix
# A = psf.projection_matrix((0, nspec), self.wavelengths, self.xyrange).toarray()
# A4 = A.reshape(self.A4.shape)
# flux1, ivar1, R1 = ex2d_patch(self.noisyimg, self.imgivar, A4, decorrelate='signal')
self.assertTrue(np.allclose(flux0, flux1))
self.assertTrue(np.allclose(ivar0, ivar1))
self.assertTrue(np.allclose(R0, R1))
#self.assertTrue(np.allclose(np.abs(flux0 - flux1)/np.sqrt(1./ivar0 + 1./ivar1), np.zeros_like(flux0)))
# Compare the "noise" decorrelation method
flux0, ivar0, R0 = specter.extract.ex2d_patch(self.noisyimg,
self.imgivar,
psf,
0,
nspec,
self.wavelengths,
xyrange=self.xyrange,
ndecorr=True)
flux1, ivar1, R1 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='noise')
self.assertTrue(np.allclose(flux0, flux1))
self.assertTrue(np.allclose(ivar0, ivar1))
self.assertTrue(np.allclose(R0, R1))
def test_basics(self):
ny, nx, nspec, nwave = self.A4.shape
img = np.random.randn(ny, nx)
imgivar = np.ones((ny, nx))
flux, varflux, R = ex2d_patch(img, imgivar, self.A4)
self.assertEqual(flux.shape, (nspec, nwave))
self.assertEqual(varflux.shape, (nspec, nwave))
self.assertEqual(R.shape, (nspec * nwave, nspec * nwave))
def test_compare_xp_cpu(self):
# Compare the "signal" decorrelation method
flux0, ivar0, R0 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='signal')
flux1, ivar1, R1 = xp_ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='signal')
self.assertTrue(np.allclose(flux0, flux1))
self.assertTrue(np.allclose(ivar0, ivar1))
self.assertTrue(np.allclose(R0, R1))
self.assertTrue(
np.allclose(
np.abs(flux0 - flux1) / np.sqrt(1. / ivar0 + 1. / ivar1),
np.zeros_like(flux0)))
# Compare the "noise" decorrelation method
flux0, ivar0, R0 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='noise')
flux1, ivar1, R1 = xp_ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='noise')
self.assertTrue(np.allclose(flux0, flux1))
self.assertTrue(np.allclose(ivar0, ivar1))
self.assertTrue(np.allclose(R0, R1))
self.assertTrue(
np.allclose(
np.abs(flux0 - flux1) / np.sqrt(1. / ivar0 + 1. / ivar1),
np.zeros_like(flux0)))
def test_compare_batch_extraction(self):
from gpu_specter.extract.gpu import _apply_weights, _batch_extraction
noisyimg_gpu = cp.asarray(self.noisyimg)
imgivar_gpu = cp.asarray(self.imgivar)
A4_gpu = cp.asarray(self.A4)
# Compare the "signal" decorrelation method
flux0, ivar0, R0 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='signal')
ny, nx, nspec, nwave = self.A4.shape
icov, y = _apply_weights(noisyimg_gpu.ravel(),
imgivar_gpu.ravel(),
A4_gpu.reshape(ny * nx, nspec * nwave),
regularize=0)
flux1_gpu, ivar1_gpu, R1_gpu = _batch_extraction(icov, y, nwave)
# Rdiags = get_resolution_diags(R, ndiag, nspectot, nwave, wavepad)[specslice[0]]
flux1 = cp.asnumpy(flux1_gpu.reshape(nspec, nwave))
ivar1 = cp.asnumpy(ivar1_gpu.reshape(nspec, nwave))
R1 = cp.asnumpy(R1_gpu.reshape(nspec * nwave, nspec * nwave))
eps_double = np.finfo(np.float64).eps
where = np.where(
~np.isclose(flux0, flux1, rtol=1e5 * eps_double, atol=0))
np.testing.assert_allclose(flux0,
flux1,
rtol=1e5 * eps_double,
atol=0,
err_msg=f"where: {where}")
self.assertTrue(
np.allclose(ivar0, ivar1, rtol=1e3 * eps_double, atol=0))
self.assertTrue(
np.allclose(np.diag(R0),
np.diag(R1),
rtol=1e2 * eps_double,
atol=1e3 * eps_double))
self.assertTrue(
np.allclose(
np.abs(flux0 - flux1) / np.sqrt(1. / ivar0 + 1. / ivar1),
np.zeros_like(flux0)))
def test_compare_xp_gpu(self):
noisyimg_gpu = cp.asarray(self.noisyimg)
imgivar_gpu = cp.asarray(self.imgivar)
A4_gpu = cp.asarray(self.A4)
# Compare the "signal" decorrelation method
flux0, ivar0, R0 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='signal')
flux1_gpu, ivar1_gpu, R1_gpu = xp_ex2d_patch(noisyimg_gpu,
imgivar_gpu,
A4_gpu,
decorrelate='signal')
flux1 = cp.asnumpy(flux1_gpu)
ivar1 = cp.asnumpy(ivar1_gpu)
R1 = cp.asnumpy(R1_gpu)
eps_double = np.finfo(np.float64).eps
where = np.where(
~np.isclose(flux0, flux1, rtol=1e5 * eps_double, atol=0))
np.testing.assert_allclose(flux0,
flux1,
rtol=1e5 * eps_double,
atol=0,
err_msg=f"where: {where}")
self.assertTrue(
np.allclose(ivar0, ivar1, rtol=1e3 * eps_double, atol=0))
self.assertTrue(
np.allclose(np.diag(R0),
np.diag(R1),
rtol=1e2 * eps_double,
atol=1e3 * eps_double))
self.assertTrue(
np.allclose(
np.abs(flux0 - flux1) / np.sqrt(1. / ivar0 + 1. / ivar1),
np.zeros_like(flux0)))
# Compare the "noise" decorrelation method
flux0, ivar0, R0 = ex2d_patch(self.noisyimg,
self.imgivar,
self.A4,
decorrelate='noise')
flux1_gpu, ivar1_gpu, R1_gpu = xp_ex2d_patch(noisyimg_gpu,
imgivar_gpu,
A4_gpu,
decorrelate='noise')
flux1 = cp.asnumpy(flux1_gpu)
ivar1 = cp.asnumpy(ivar1_gpu)
R1 = cp.asnumpy(R1_gpu)
self.assertTrue(
np.allclose(flux0, flux1, rtol=1e5 * eps_double, atol=0))
self.assertTrue(
np.allclose(ivar0, ivar1, rtol=1e3 * eps_double, atol=0))
self.assertTrue(
np.allclose(np.diag(R0),
np.diag(R1),
rtol=1e2 * eps_double,
atol=0))
self.assertTrue(
np.allclose(
np.abs(flux0 - flux1) / np.sqrt(1. / ivar0 + 1. / ivar1),
np.zeros_like(flux0)))