diff options
Diffstat (limited to 'fg21sim')
| -rw-r--r-- | fg21sim/utils/reproject.py | 54 |
1 files changed, 51 insertions, 3 deletions
diff --git a/fg21sim/utils/reproject.py b/fg21sim/utils/reproject.py index cbadc53..b0e78ba 100644 --- a/fg21sim/utils/reproject.py +++ b/fg21sim/utils/reproject.py @@ -170,6 +170,52 @@ def _image_to_healpix(image, wcs, nside, order=1, hemisphere=None): return hpdata +def _inpaint_healpix(data, ipix): + """Inpaint the missing pixels in the HEALPix map. + + The missing pixels are filled with the averages of corresponding + 8 neighboring pixels. + The other missing pixels within the 8 neighboring pixels are also excluded + when calculating the averages. + + Parameters + ---------- + data : 1D `~numpy.ndarray` + HEALPix data array in *RING* ordering + ipix : 1D `~numpy.ndarray` + Array specifying the missing pixels in the HEALPix map. + This can be either an integer array of missing pixel indices + in *RING* ordering, or be an boolean array of same length with `True` + indicating the missing pixels. + + Returns + ------- + inpainted : 1D `~numpy.ndarray` + Inpainted HEALPix data array + """ + logger.info("Inpaint the HEALPix map for missing pixels ...") + if (ipix.dtype == np.bool) and (ipix.shape == data.shape): + ipix = np.where(ipix)[0] + nside = hp.npix2nside(data.size) + inpainted = data.copy() + inpainted[ipix] = np.nan + logger.info("Get the 8 neighbors for each HEALPix missing pixel") + ipix_neighbors = hp.get_all_neighbours(nside, ipix) + neighbors = inpainted[ipix_neighbors] + # Warn the pixels with >4 NaN's within their 8 neighbors + neighbors_nan = np.sum(np.isnan(neighbors), axis=0) + ipix_warning = ipix[neighbors_nan > 4] + if len(ipix_warning) > 0: + logger.warning("These pixels whose 8 neighbors have >4 NaN's: " + "%s" % (", ".join(np.char.mod("%d", ipix_warning)))) + # + logger.info("Calculate the averages of neighbors (exclude NaN's)") + averages = np.nanmean(neighbors, axis=0) + inpainted[ipix] = averages + logger.info("Done inpaint %d missing pixels" % len(ipix)) + return inpainted + + def zea2healpix(img1, img2, nside, order=1, inpaint=False, append_history=None, append_comment=None): """Reproject the maps in ZEA (zenithal/azimuthal equal area) projection @@ -215,7 +261,8 @@ def zea2healpix(img1, img2, nside, order=1, inpaint=False, full-sky HEALPix map. Duplicate/overlapping pixels are warned. - The combined full-sky HEALPix map may still have some missing pixels, which is also warned. - TODO: inpaint the missing pixels by interpolation + If `inpaint=True`, then these missing pixels are filled with the + averages of their 8 neighboring pixels (exclude NaN's). """ if isinstance(img1, str): img1 = fits.open(img1)[0] @@ -250,12 +297,13 @@ def zea2healpix(img1, img2, nside, order=1, inpaint=False, logger.warning("Reprojected HEALPix data has %d duplicate pixel(s)" % pix_dup.sum()) hp_data[pix_dup] /= 2.0 - logger.warning("Averaged the duplicate pixel(s)") + logger.info("Averaged the duplicate pixel(s)") pix_missing = (hp_mask == 0) if pix_missing.sum() > 0: logger.warning("Reprojected HEALPix data has %d missing pixel(s)" % pix_missing.sum()) - # XXX/TODO: inpaint + if inpaint: + hp_data = _inpaint_healpix(hp_data, pix_missing) # HEALPix FITS header header = zea_hdr1.copy(strip=True) keys = ["CTYPE1", "CTYPE2", "CRPIX1", "CRPIX2", "CRVAL1", "CRVAL2", |