def test_draw_hires(self):
""" Draw a partial healpix map with very large nside """
nside = 4096 * 2**5
ra, dec = 45, -45
radius = 0.05
pixels = skymap.healpix.ang2disc(nside, ra, dec, radius)
values = pixels
plt.figure()
# Use the Cassini projection (because we can)
m = Skymap(projection='cass',
lon_0=ra,
lat_0=dec,
celestial=False,
llcrnrlon=ra + 2 * radius,
urcrnrlon=ra - 2 * radius,
llcrnrlat=dec - 2 * radius,
urcrnrlat=dec + 2 * radius)
m.draw_hpxmap(values, pixels, nside=nside, xsize=400)
m.draw_parallels(np.linspace(dec - 2 * radius, dec + 2 * radius, 5),
labelstyle='+/-',
labels=[1, 0, 0, 0])
m.draw_meridians(np.linspace(ra - 2 * radius, ra + 2 * radius, 5),
labelstyle='+/-',
labels=[0, 0, 0, 1])
plt.title('HEALPix Zoom (nside=%i)' % nside)
overwrite=True,
nest=True,
fits_IDL=False,
partial=True)
#Make plot of the healpix map
upix, uval = [], []
for jj, vv in enumerate(hp_aux):
if vv != hp.UNSEEN:
upix.append(jj)
uval.append(vv)
upix = np.array(upix)
uval = np.array(uval)
k = hp.nest2ring(4096, upix)
m = Skymap(projection='cass',
lon_0=67.,
lat_0=-60.,
celestial=False,
llcrnrlon=56.68,
urcrnrlon=77.46,
llcrnrlat=-64.2,
urcrnrlat=-55.40,
parallels=False,
meridians=False)
m.draw_hpxmap(uval, k, nside=4096, xsize=1000)
plt.savefig('healpix_imageLow.png')
paint = np.array(paint)
pix = np.array(pix)
m = Skymap(projection='cass',
lon_0=67.,
lat_0=-60.,
celestial=False,
llcrnrlon=56.68,
urcrnrlon=77.46,
llcrnrlat=-64.2,
urcrnrlat=-55.40,
parallels=False,
meridians=False)
da = m.draw_hpxmap(paint, pix, nside=512, xsize=1000)
plt.colorbar()
plt.savefig('healpix_dra.png')
plt.close()
paintRA = paint.copy()
#New Dra
pix = []
paint = []
for j, val in enumerate(draN):
if val != hp.UNSEEN:
pix.append(j)
paint.append(val)
def main(survey, plot=True, write=True):
print("Creating mask with NSIDE={} and NEST={}...".format(NSIDE, NEST))
# Initiate_mask
if args.load is not None:
healpix_mask = ugali.utils.healpix.read_map(args.load, nest=NEST)
else:
healpix_mask = np.tile(0, hp.nside2npix(NSIDE))
infile_dust = 'ebv_sfd98_fullres_nside_4096_nest_equatorial.fits.gz'
ebv_map = ugali.utils.healpix.read_map(infile_dust, nest=True)
cut_ebv = (ebv_map > 0.2)
healpix_mask[cut_ebv] |= 0b00001
"""
McConnachie15: Half-light radius along major axis. Could incorporate ellipticity info
Harris96: Half-light radius
Corwen04: No radius data
Nilson73: Major axis (no ellipticity info)
Webbink85: 10**log(radius)
Kharchenko13: Radius of the central part
Bica08: Major axis. Could incorporate ellipticity info
WEBDA: Diameter/2
ExtraDwarfs: Half-light radius
ExtraClusters: Half-light radius
Vizier data on: Nilson73, Webbink85 (should check log or ln), Kharchenko13 (3 radii, used middle), Bica08
ugali data on: McConnachi15, Harris96, WEBDA14
"""
external_cat_list = [
'Harris96', 'Corwen04', 'Nilson73', 'Webbink85', 'Kharchenko13',
'Bica08', 'WEBDA14', 'ExtraClusters', 'ExtraStructures'
]
for external_cat in external_cat_list:
if args.mode == 1:
catalog = ugali.candidate.associate.catalogFactory(external_cat)
external_cut = cut_circles(catalog['ra'],
catalog['dec'],
default_radius=0.1)
elif args.mode == 2:
catalog = associate.catalogFactory(external_cat)
external_cut = cut_circles(catalog['ra'],
catalog['dec'],
catalog['radius'],
default_radius=0.1)
healpix_mask[external_cut] |= 0b00010
known_dwarfs = ['McConnachie15', 'ExtraDwarfs']
for external_cat in known_dwarfs:
if args.mode == 1:
catalog = ugali.candidate.associate.catalogFactory(external_cat)
external_cut = cut_circles(catalog['ra'],
catalog['dec'],
default_radius=0.1)
elif args.mode == 2:
catalog = associate.catalogFactory(external_cat)
external_cut = cut_circles(catalog['ra'],
catalog['dec'],
catalog['radius'],
default_radius=0.1)
healpix_mask[external_cut] |= 0b00100
reader_bsc = pyfits.open('bsc5.fits')
d_bsc = reader_bsc[1].data
bsc_cut = cut_circles(d_bsc['RA'], d_bsc['DEC'], default_radius=0.1)
healpix_mask[bsc_cut] |= 0b01000
if 'des' == survey:
des_footprint = ugali.utils.healpix.read_map(
'y3a2_footprint_griz_1exp_v2.0.fits.gz', nest=True)
#print('Masking footprint...')
#npix = hp.nside2npix(NSIDE)
#nbad = 3
#des_cut = np.fromiter( (des_footprint[i] < 1 or sum(des_footprint[hp.get_all_neighbours(NSIDE, i)] < 1) >= nbad for i in range(npix)), dtype=bool, count=npix ) # Mask if at least nbad neighbors outside the footprint
des_cut = des_footprint < 1
healpix_mask[des_cut] |= 0b10000
if 'ps1' == survey:
ps1_cut = hp.query_strip(
NSIDE,
np.radians(90.0 - -25.0),
np.radians(90.0 - -90.0),
nest=False
) # This function apparently isn't implemented for nest=True
ps1_cut = hp.ring2nest(NSIDE, ps1_cut)
healpix_mask[ps1_cut] |= 0b10000
#failures = pyfits.open('ugali_failures.fits')[1].data # NSIDE = 256
#fail_pix_256 = [ugali.utils.healpix.angToPix(256, fail['ra'], fail['dec'], nest=NEST) for fail in failures]
#fail_pix = ugali.utils.healpix.ud_grade_ipix(fail_pix_256, 256, NSIDE, nest=NEST)
#healpix_mask[fail_pix] |= 0b100000
# Artifact masks
artifact_coords = np.array([
(278.70, 38.67), # J1834.8+3840, obvious masking issue
(35.01, -3.04), # J0220.1-0302, psf failures
(201.59, -11.47), # J1326.4-1128, obvious masking issue
(214.52, 19.20), # J1418.1+1912, obvious masking issue
#(131.50, 28.57), # J0846.0+2834, ???
(262.66, 52.11), # J1730.7+5206, likely artifact
#(169.99, -14.96), # J1120.0-1457, ???
(186.55, 46.17), # J1226.2+4609, likely artifact
(145.22, -13.74), # J0940.9-1344, likely artifact
(28.11, 19.39), # J0152.5+1923, no uglai source?
#(247.73, -0.97), # J1630.9+0058, real star cluster
(19.40, -17.44)
]) # J0117.6-1726, Cetus II
#(39.88, 0.18)]) # J0239.5+0010,
artifact_ras, artifact_decs = artifact_coords.T
artifact_cut = cut_circles(artifact_ras,
artifact_decs,
default_radius=0.1)
healpix_mask[artifact_cut] |= 0b1000000
if plot:
print("Simplifying mask for plotting...")
simplified_mask = np.copy(healpix_mask)
cut_catalog = np.where((simplified_mask & 0b00010)
| (simplified_mask & 0b00100)
| (simplified_mask & 0b01000))
cut_ebv = np.where(simplified_mask & 0b00001)
cut_footprint = np.where((simplified_mask & 0b10000)
| (simplified_mask & 0b100000)
| (simplified_mask & 0b1000000))
simplified_mask[cut_catalog] = 1
simplified_mask[cut_ebv] = 2
simplified_mask[cut_footprint] = 3
print("Plotting...")
title = ''
if survey == 'des':
title = 'DES ' + title
elif survey == 'ps1':
title = 'Pan-STARRS ' + title
"""
# Using mollview
hp.mollview(simplified_mask, nest=True, coord='C', cmap=new_cmap, title=title, xsize=1600)
ax = plt.gca()
cbar = ax.images[-1].colorbar
cbar.set_ticks( (np.arange(n) + 0.5)*(n-1)/n )
cbar.set_ticklabels(['Unmasked', 'Association', r'$E(B-V)$', 'Footprint'])
hp.graticule()
plt.savefig('healpix_mask_{}_v{}.png'.format(survey, version), bbox_inches='tight')
"""
# Using skymap
simplified_mask_ring = hp.reorder(simplified_mask, n2r=True)
fig, ax = plt.subplots(figsize=(12, 8))
smap = Skymap(projection='mbtfpq', lon_0=0)
im, lon, lat, values = smap.draw_hpxmap(simplified_mask_ring,
xsize=1600,
cmap=new_cmap)
cbar = plt.colorbar(ticks=(np.arange(n) + 0.5) * (n - 1) / n,
fraction=0.02)
cbar.set_ticklabels(
['Unmasked', 'Association', r'$E(B-V) > 0.2$', 'Footprint'])
plt.title(title)
plt.savefig('healpix_mask_{}_v{}.png'.format(survey, version),
bbox_inches='tight')
if write:
print('Writing mask to healpix_mask_{}_v{}.fits.gz ...'.format(
survey, version))
hp.write_map('healpix_mask_{}_v{}.fits.gz'.format(survey, version),
healpix_mask,
dtype=np.int32,
nest=NEST,
coord='C',
overwrite=True)
return healpix_mask, simplified_mask_ring
if args.survey is None:
des_mask, des_mask_simplified = main('des',
plot=(not args.no_plot),
write=(not args.no_write))
ps1_mask, ps1_mask_simplified = main('ps1',
plot=(not args.no_plot),
write=(not args.no_write))
# Put the two healpix masks on the same plot
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 10))
plt.sca(axes[0])
smap = Skymap(projection='mbtfpq', lon_0=0)
im, lon, lat, values = smap.draw_hpxmap(des_mask_simplified,
xsize=1600,
cmap=new_cmap)
plt.title('DES', fontsize='large')
plt.sca(axes[1])
smap = Skymap(projection='mbtfpq', lon_0=0)
im, lon, lat, values = smap.draw_hpxmap(ps1_mask_simplified,
xsize=1600,
cmap=new_cmap)
plt.title('Pan-STARRS', fontsize='large')
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.175, 0.020, 0.65])
cbar = fig.colorbar(im,
cax=cbar_ax,
ticks=(np.arange(n) + 0.5) * (n - 1) / n)
cbar.set_ticklabels(
['Unmasked', 'Association', r'$E(B-V) > 0.2$', 'Footprint'])