def query_des_fits(ra,
dec,
size=10.0,
output_file='DES_TMP.fits',
xpix=500,
ypix=500,
projection='AIT'):
coord = SkyCoord(ra, dec, unit='deg', frame='icrs')
fov = str(round(size / 60 * 100) / 100) + ' deg'
geometry = WCSGeometry.create(skydir=coord,
width=xpix,
height=ypix,
fov=fov,
coordsys='icrs',
projection=projection)
hips_survey_base = 'CDS/P/DES-DR1/'
#Split string in a list and search for fits image URLs & band names
bands_list = ['g', 'r', 'i', 'z', 'Y']
for i in range(0, len(bands_list)):
try:
print("Downloading band " + bands_list[i] + ": " + bands_list[i] +
'_' + output_file)
hips_survey = hips_survey_base + bands_list[i]
result = make_sky_image(geometry, hips_survey, 'fits')
result.write_image(bands_list[i] + '_' + output_file)
except:
print("Failed")
def __init__(self):
#projections support http://docs.astropy.org/en/stable/wcs/#supported-projections
ra = 214.641841
dec = 51.2152
geometry = WCSGeometry.create(
skydir=SkyCoord(ra, dec, unit='deg'),
width=100,
height=100,
fov="0.03 deg",
coordsys='icrs',
projection='SIN',
)
coor = SkyCoord(ra, dec, unit='deg', frame='galactic')
print(coor.to_string('hmsdms'))
# geometry = WCSGeometry.create(
# skydir=SkyCoord(280.4652, -32.8884, unit='deg', frame='galactic'),
# width=1600, height=1000, fov="2 deg",
# coordsys='icrs', projection='SIN',
# )
# hips_survey = HipsSkyMaps.getMap("CFHT")
hips_survey = 'CDS/P/SPITZER/MIPS2'
hips_survey = 'CDS/P/CFHTLS/W/r'
#"P/CFHTLS/W/r) 14 18 42.36 +51 25 56.7 0.5 deg"
#result = make_sky_image(geometry, hips_survey, 'jpeg')
result = make_sky_image(geometry,
hips_survey,
'fits',
progress_bar=False)
image = result.image
plt.clf()
fig1 = plt.gcf()
w = h = 3.125 # 300pixel = 3.125inches (1in == 2.54cm) 1 inch = 96 pixel
fig1.set_size_inches(w, h)
plt.imshow(result.image,
origin='lower',
interpolation='none',
cmap=plt.get_cmap("Greys"),
norm=matplotlib.colors.LogNorm(vmin=0.1,
vmax=result.image.max()))
plt.axis("off")
#plt.figure(figsize=(20, 10))
result.plot()
plt.show()
"""Basic example how to plot a sky image with the hips package"""
from astropy.coordinates import SkyCoord
from hips import WCSGeometry, make_sky_image
# Compute the sky image
geometry = WCSGeometry.create(
skydir=SkyCoord(0, 0, unit='deg', frame='galactic'),
width=2000, height=1000, fov="3 deg",
coordsys='galactic', projection='AIT',
)
hips_survey = 'CDS/P/DSS2/red'
result = make_sky_image(geometry=geometry, hips_survey=hips_survey, tile_format='fits')
result.plot()
# Draw the sky image
# import matplotlib.pyplot as plt
# from astropy.visualization.mpl_normalize import simple_norm
# ax = plt.subplot(projection=geometry.wcs)
# norm = simple_norm(result.image, 'sqrt', min_percent=1, max_percent=99)
# ax.imshow(result.image, origin='lower', norm=norm, cmap='gray')
# import matplotlib.pyplot as plt
# plt.show()
# In[ ]:
print(url)
# In[ ]:
hips_survey_property = HipsSurveyProperties.fetch(url)
# In[ ]:
geometry = WCSGeometry.create(skydir=SkyCoord(217.9,
-2.3,
unit='deg',
frame='fk5'),
width=180,
height=180,
fov='0.02 deg',
coordsys='icrs',
projection='TAN')
from hips import make_sky_image
result = make_sky_image(geometry, hips_survey_property, 'fits')
# In[ ]:
result.plot()
# In[ ]:
#result.image
def plot_hips_sky_image(ra: float, dec: float, sig_p: float, sigma1: float,
hips_surveys: List, outpath: str, name: str,
label: int, res: int):
""" Plot sky image using hips
: ra : ra of the pixel
: dec : dec of the pixel
: sig_p : sig_poisson of the pixel
: sigma1 : sigma1 of the map
: hips_surveys : list of surveys
: outpath : output dir path
: name : name of the system (dwarf and more info)
: label : label of a cluster pixels
: res : resolution of the image (number of pixels for the image)
"""
width = WIDTH_FAC * sigma1
# Compute the sky image
geometry = WCSGeometry.create(skydir=SkyCoord(ra,
dec,
unit='deg',
frame='icrs'),
width=res,
height=res,
fov="%f deg" % width,
coordsys='icrs',
projection='AIT')
name_split = name.split('-')
short_name = name_split[0]
data = np.load('results/{}'.format(
name.replace('-poisson' or '-gaussian', '/queried-data.npy'))).item()
ra_data, dec_data = data['ra'], data['dec']
pmra_data, pmdec_data = data['pmra'], data['pmdec']
bp_rp_data, g_mag_data = data['bp_rp'], data['phot_g_mean_mag']
data = None # free memory
ra_min = ra - 0.5 * width
ra_max = ra + 0.5 * width
dec_min = dec - 0.5 * width
dec_max = dec + 0.5 * width
mask1 = (ra_min < ra_data) & (ra_data < ra_max)
mask2 = (dec_min < dec_data) & (dec_data < dec_max)
mask = mask1 & mask2
ra_data, dec_data = ra_data[mask], dec_data[mask]
pmra_data, pmdec_data = pmra_data[mask], pmdec_data[mask]
bp_rp_data, g_mag_data = bp_rp_data[mask], g_mag_data[mask]
is_in = dist2(ra_data, dec_data, ra, dec) <= sigma1**2
n_star_in = len(ra_data[is_in])
if n_star_in < NSTAR_MIN:
_s1 = 'skipping image for %s ' % short_name
_s2 = 'because there are only %d stars in the kernel' % n_star_in
print(_s1 + _s2)
return # skip plotting image with fewer than NSTAR_MIN stars
print('plotting image for %s' % short_name)
sns.set(style="white", color_codes=True, font_scale=1)
if 'gaia' in short_name:
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
name_hue = ['inside' if i else 'outside' for i in is_in]
ms = 20
circle = plt.Circle((ra, dec),
sigma1,
color='k',
fill=False,
lw=2,
alpha=0.6)
axes[0, 0].add_artist(circle)
sns.scatterplot(x=ra_data,
y=dec_data,
s=ms,
hue=name_hue,
ax=axes[0, 0])
sns.scatterplot(x=bp_rp_data,
y=g_mag_data,
s=ms,
hue=name_hue,
ax=axes[0, 1])
sns.scatterplot(x=pmra_data,
y=pmdec_data,
s=ms,
hue=name_hue,
ax=axes[0, 2])
sns.scatterplot(x=ra_data[is_in],
y=dec_data[is_in],
s=ms,
ax=axes[0, 0])
sns.scatterplot(x=bp_rp_data[is_in],
y=g_mag_data[is_in],
s=ms,
ax=axes[0, 1])
sns.scatterplot(x=pmra_data[is_in],
y=pmdec_data[is_in],
s=ms,
ax=axes[0, 2])
axes[0, 0].set_title('stellar distribution')
axes[0, 0].set_xlabel('ra (deg)')
axes[0, 0].set_ylabel('dec (deg)')
axes[0, 0].set_xlim([ra_min, ra_max])
axes[0, 0].set_ylim([dec_min, dec_max])
axes[0, 0].invert_xaxis()
axes[0, 1].set_title('color mag diagram (CMD)')
axes[0, 1].invert_yaxis()
axes[0, 1].set_xlabel('Bp-Rp (mag)')
axes[0, 1].set_ylabel('G (mag)')
axes[0, 2].set_title('proper motions')
axes[0, 2].set_xlabel('pmra (mas/yr)')
axes[0, 2].set_ylabel('pmdec (mas/yr)')
for u in range(3):
for v in range(2):
_asp = np.diff(axes[0, u].get_xlim())[0]
_asp /= np.diff(axes[0, u].get_ylim())[0]
axes[1, u].set_aspect(np.abs(_asp))
cnt = 0 # counter for how many images have been plotted
for hips_survey in hips_surveys:
try:
result = make_sky_image(geometry=geometry,
hips_survey=hips_survey,
tile_format='jpg',
progress_bar=False)
axes[1, cnt].imshow(result.image, origin='lower')
axes[1, cnt].set_title(hips_survey)
cnt += 1
except:
pass
if cnt == 3:
break
for u in range(3):
axes[1, u].tick_params(axis='both',
which='both',
labelleft=False,
labelbottom=False)
else:
fig, axes = plt.subplots(1, 4, figsize=(15, 5))
# plot sources
axes[0].set_title(short_name)
sns.scatterplot(ra_data, dec_data, ax=axes[0])
axes[0].set_xlim([ra_min, ra_max])
axes[0].set_ylim([dec_min, dec_max])
_asp = np.diff(axes[0].get_xlim())[0] / np.diff(axes[0].get_ylim())[0]
axes[0].set_aspect(_asp)
axes[0].set_xlim(axes[0].set_xlim([ra_min, ra_max])[::-1]) # flipping
# plot circle of the size of sigma1
circle = plt.Circle((ra, dec),
sigma1,
color='orange',
fill=False,
lw=2)
axes[0].add_artist(circle)
cnt = 0 # counter for how many images have been plotted
for hips_survey in hips_surveys:
try:
result = make_sky_image(geometry=geometry,
hips_survey=hips_survey,
tile_format='jpg',
progress_bar=False)
cnt += 1
axes[cnt].imshow(result.image, origin='lower')
axes[cnt].set_title(hips_survey)
except:
pass
if cnt == 3:
break
for i in range(4):
axes[i].tick_params(axis='both',
which='both',
labelleft=False,
labelbottom=False)
_st1 = '{}-label{}-sigp%0.2f-'.format(short_name, label) % sig_p
_st2 = '-width{}'.format(width)
fig.suptitle('{}(%0.4f,%0.4f){}'.format(_st1, _st2) % (ra, dec))
_str = '{}/{}-target{}-'.format(outpath, name, label)
_str = '{}ra%0.4f-dec%0.4f-sigp%0.2f.jpg'.format(_str) % (ra, dec, sig_p)
plt.savefig(_str, bbox_inches='tight', dpi=300)