added extra files for test_renderer.py

test_resampling/galsim_hst_to_hsc_resolution.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a7a29fac7782b7a9fd19b66cebae724f95c9c4f0bfb477aa2d4f1483dee2149
+size 10128

test_resampling/galsim_hst_to_hsc_resolution.py

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+
+import warnings
+warnings.filterwarnings('ignore')
+
+import numpy as np
+import scarlet2
+import galsim
+
+import numpy as np
+import astropy.io.fits as fits
+from astropy.wcs import WCS
+
+import jax.numpy as jnp
+
+def generate_hst_to hsc_galsim():
+    # Load the HSC image data
+    obs_hdu = fits.open('../../scarlet/data/test_resampling/Cut_HSC1.fits')
+    data_hsc = jnp.array(obs_hdu[0].data.byteswap().newbyteorder(), jnp.float32)
+    wcs_hsc = WCS(obs_hdu[0].header)
+    channels_hsc = ['g','r','i','z','y']
+
+    # Load the HSC PSF data
+    psf_hsc_data = fits.open('../../scarlet/data/test_resampling/PSF_HSC.fits')[0].data.astype('float32')
+    Np1, Np2 = psf_hsc_data[0].shape
+    psf_hsc_data = jnp.pad(psf_hsc_data, ((0,0), (1,0), (1,0)))
+    psf_hsc = scarlet2.ArrayPSF(psf_hsc_data)
+
+    # Load the HST image data
+    hst_hdu = fits.open('../../scarlet/data/test_resampling/Cut_HST1.fits')
+    data_hst = hst_hdu[0].data
+    wcs_hst = WCS(hst_hdu[0].header)
+    channels_hst = ['F814W']
+
+    # Load the HST PSF data
+    psf_hst = fits.open('../../scarlet/data/test_resampling/PSF_HST.fits')[0].data
+    psf_hst = np.array(psf_hst[None,:,:], np.float32)
+    psf_hst = jnp.pad(psf_hst, ((0,0), (1,0), (1,0)))
+    psf_hst = np.repeat(psf_hst, 5, 0)
+
+    psf_hst = scarlet2.ArrayPSF(psf_hst)
+
+    # Scale the HST data
+    n1,n2 = np.shape(data_hst)
+    data_hst = data_hst.reshape(1, n1, n2).byteswap().newbyteorder()
+    data_hst *= data_hsc.max() / data_hst.max()
+
+    r, N1, N2 = data_hsc.shape
+
+    # define two observation packages and match to frame
+    obs_hst = scarlet2.Observation(data_hst[:1, ...],
+                                wcs=wcs_hst,
+                                psf=psf_hst,
+                                channels=['channel'],
+                                weights=None)
+
+    obs_hsc = scarlet2.Observation(data_hsc[:1,...],
+                                wcs=wcs_hsc,
+                                psf=psf_hsc,
+                                channels=['channel'],
+                                weights=None)
+
+    # Initialize images
+    h_hst = 0.03
+    h_hsc = 0.168
+
+    gi_hst_im = galsim.Image(np.array(data_hst[0]), scale=h_hst)
+
+    gii_hst_im = galsim.InterpolatedImage(gi_hst_im, 
+                                    x_interpolant=galsim.Quintic(),
+                                k_interpolant=galsim.Quintic()
+                                )
+
+    hst_psf = psf_hst()[0]
+
+    gi_hst_psf = galsim.Image(np.array(hst_psf), scale=h_hst)
+    gii_hst_psf = galsim.InterpolatedImage(gi_hst_psf,
+                                x_interpolant=galsim.Quintic(),
+                                k_interpolant=galsim.Quintic()
+                                )
+
+    hsc_psf = psf_hsc()[0]
+
+    gi_hsc_psf = galsim.Image(np.array(hsc_psf), scale=h_hsc)
+    gii_hsc_psf = galsim.InterpolatedImage(gi_hsc_psf,
+                                x_interpolant=galsim.Quintic(),
+                                k_interpolant=galsim.Quintic()
+                                )
+
+    # Deconvolution and Reconvolution
+    inv_gii_hst_psf = galsim.Deconvolve(gii_hst_psf)
+    deconv_hst = galsim.Convolve(inv_gii_hst_psf, gii_hst_im)
+    reconv_hsc = galsim.Convolve(gii_hsc_psf, deconv_hst)
+
+    out_galsim = reconv_hsc.drawImage(nx=N1, ny=N2, scale=0.168, method='no_pixel')
+    np.save('./galsim_hst_to_hsc_resolution.npy', out_galsim.array)