added multires tutorial files

multiresolution_tutorial/data.fits.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:834d93bee156e7bb0e6bf28c8be394beba6e246d1512b002097e684b1fa46616
+size 375290

multiresolution_tutorial/get_source_catalog.py

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+
+import os
+from jax import vmap
+import jax.numpy as jnp
+import numpy as np
+
+import astropy.io.fits as fits
+from astropy.wcs import WCS
+
+import scarlet2
+from scarlet2 import Starlet
+
+# Installing data package if not already installed
+from scarlet2.utils import import_scarlet_test_data
+
+import_scarlet_test_data()
+from scarlet_test_data import data_path
+
+import sep
+
+from astropy.table import Table
+
+# Load the HSC image data
+obs_hdu = fits.open(os.path.join(data_path, "test_resampling", "Cut_HSC1.fits"))
+data_hsc = obs_hdu[0].data.astype('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(os.path.join(data_path, "test_resampling", "PSF_HSC.fits"))[0].data.astype('float32')
+psf_hsc = scarlet2.ArrayPSF(psf_hsc_data)
+
+# Load the HST image data
+hst_hdu = fits.open(os.path.join(data_path, "test_resampling", "Cut_HST1.fits"))
+data_hst = hst_hdu[0].data.astype('float32')
+wcs_hst = WCS(hst_hdu[0].header)
+channels_hst = ['F814W']
+
+# Load the HST PSF data
+psf_hst_data = fits.open(os.path.join(data_path, "test_resampling", "PSF_HST.fits"))[0].data.astype('float32')
+psf_hst_data = psf_hst_data[None, :, :]
+psf_hst = scarlet2.ArrayPSF(psf_hst_data)
+
+# Scale the HST data
+data_hst = data_hst[None, ...].astype('float32')
+data_hst *= data_hsc.max() / data_hst.max()
+
+"""
+Next we have to create a source catalog for the images. We’ll use `sep` for that, 
+but any other detection method will do. Since HST is higher resolution and less affected by blending, 
+we use it for detection but we also run detection on the HSC image to calculate the background RMS
+"""
+
+def makeCatalog(data_lr, data_hr, lvl=3, wave=True):
+    # Create a catalog of detected source by running SEP on the wavelet transform
+    # of the sum of the high resolution images and the low resolution images interpolated 
+    # to the high resolution grid
+
+    coords_in = jnp.stack(jnp.meshgrid(jnp.linspace(0, 1, data_lr.shape[-1] + 2)[1:-1],
+                                       jnp.linspace(0, 1, data_lr.shape[-2] + 2)[1:-1]), -1)
+
+    coords_out = jnp.stack(jnp.meshgrid(jnp.linspace(0, 1, data_hr.shape[-2] + 2)[1:-1],
+                                      jnp.linspace(0, 1, data_hr.shape[-2] + 2)[1:-1]), -1)
+
+    interp_im = vmap(scarlet2.interpolation.resample2d,
+                         in_axes=(0, None, None))(data_lr, coords_in, coords_out)
+
+    # Normalisation
+    interp_im = interp_im / jnp.sum(interp_im, axis=(1, 2))[:, None, None]
+    hr_images = data_hr / jnp.sum(data_hr, axis=(1, 2))[:, None, None]
+
+    # Summation to create a detection image
+    detect_image = jnp.sum(interp_im, axis=0) + jnp.sum(hr_images, axis=0)
+    # Rescaling to HR image flux
+    detect_image *= jnp.sum(data_hr)
+    # Wavelet transform
+    wave_detect = Starlet.from_image(detect_image).coefficients
+
+    if wave:
+        # Creates detection from the first 3 wavelet levels
+        detect = wave_detect[:lvl, :, :].sum(axis=0)
+    else:
+        detect = detect_image
+
+    # Runs SEP detection
+    bkg = sep.Background(np.array(detect))
+    catalog = sep.extract(np.array(detect), 3, err=bkg.globalrms)
+    bg_rms = []
+    for img in [np.array(data_lr), np.array(data_hr)]:
+        if np.size(img.shape) == 3:
+            bg_rms.append(np.array([sep.Background(band).globalrms for band in img]))
+        else:
+            bg_rms.append(sep.Background(img).globalrms)
+
+    return catalog, bg_rms, detect_image
+
+# Making catalog.
+# With the wavelet option on, only the first 3 wavelet levels are used for detection. Set to 1 for better detection
+wave = 1
+lvl = 3
+catalog_hst, (bg_hsc, bg_hst), detect = makeCatalog(data_hsc, data_hst, lvl, wave)
+
+
+# we can now set the empirical noise rms for both observations
+obs_hst_weights = np.ones(data_hst.shape) / (bg_hst ** 2)[:, None, None]
+obs_hsc_weights = np.ones(data_hsc.shape) / (bg_hsc ** 2)[:, None, None]
+
+obs_hst = scarlet2.Observation(data_hst,
+                               wcs=wcs_hst,
+                               psf=psf_hst,
+                               channels=channels_hst,
+                               weights=obs_hst_weights)
+
+pixel_hst = np.stack((catalog_hst['y'], catalog_hst['x']), axis=1)
+# Convert the HST pixel coordinates to sky coordinates
+coords = obs_hst.frame.get_sky_coord(pixel_hst)
+
+# Saving everything in a single fits file
+
+# Coordinates table (FK5 J2000.0)
+table = Table([coords.ra.deg, coords.dec.deg], names=('RA', 'DEC'))
+coord_hdu = fits.BinTableHDU(data=table)
+coord_hdu.header['RADECSYS'] = coords.frame.name.upper() # 'FK5'
+coord_hdu.header['EQUINOX'] = coords.equinox.jyear # 2000.0
+
+# Build HDUs images
+primary_hdu = fits.PrimaryHDU(data=data_hsc, header=wcs_hsc.to_header())  # Observation 1 Image
+primary_hdu.header['EXTNAME'] = 'HSC_OBS'
+
+psf1_hdu = fits.ImageHDU(data=psf_hsc_data)
+psf1_hdu.header['EXTNAME'] = 'HSC_PSF'
+
+weight1_hdu = fits.ImageHDU(data=obs_hsc_weights)
+weight1_hdu.header['EXTNAME'] = 'HSC_WEIGHTS'
+
+image2_hdu = fits.ImageHDU(data=data_hst, header=wcs_hst.to_header())
+image2_hdu.header['EXTNAME'] = 'HST_OBS'
+
+psf2_hdu = fits.ImageHDU(data=psf_hst_data)
+psf2_hdu.header['EXTNAME'] = 'HST_PSF'
+
+weight2_hdu = fits.ImageHDU(data=obs_hst_weights)
+weight2_hdu.header['EXTNAME'] = 'HST_WEIGHTS'
+
+coord_hdu.header['EXTNAME'] = 'CATALOG'
+
+# Bundle all HDUs
+hdulist = fits.HDUList([
+    primary_hdu,
+    psf1_hdu,
+    weight1_hdu,
+    image2_hdu,
+    psf2_hdu,
+    weight2_hdu,
+    coord_hdu
+])
+
+hdulist.writeto('multiresolution_tutorial_data.fits', overwrite=True)