def test_python_kdtree(monkeypatch):
from astropy.coordinates import ICRS
cmatch = ICRS([4, 2.1]*u.degree, [0, 0]*u.degree, distance=[1, 2]*u.kpc)
ccatalog = ICRS([1, 2, 3, 4]*u.degree, [0, 0, 0, 0]*u.degree, distance=[1, 2, 3, 4]*u.kpc)
monkeypatch.delattr("scipy.spatial.cKDTree")
with pytest.warns(UserWarning, match='C-based KD tree not found'):
matching.match_coordinates_sky(cmatch, ccatalog)
def test_matching_method():
from astropy.coordinates import ICRS, SkyCoord
from astropy.utils import NumpyRNGContext
from astropy.coordinates.matching import match_coordinates_3d, match_coordinates_sky
with NumpyRNGContext(987654321):
cmatch = ICRS(
np.random.rand(20) * 360. * u.degree,
(np.random.rand(20) * 180. - 90.) * u.degree)
ccatalog = ICRS(
np.random.rand(100) * 360. * u.degree,
(np.random.rand(100) * 180. - 90.) * u.degree)
idx1, d2d1, d3d1 = SkyCoord(cmatch).match_to_catalog_3d(ccatalog)
idx2, d2d2, d3d2 = match_coordinates_3d(cmatch, ccatalog)
npt.assert_array_equal(idx1, idx2)
assert_allclose(d2d1, d2d2)
assert_allclose(d3d1, d3d2)
# should be the same as above because there's no distance, but just make sure this method works
idx1, d2d1, d3d1 = SkyCoord(cmatch).match_to_catalog_sky(ccatalog)
idx2, d2d2, d3d2 = match_coordinates_sky(cmatch, ccatalog)
npt.assert_array_equal(idx1, idx2)
assert_allclose(d2d1, d2d2)
assert_allclose(d3d1, d3d2)
assert len(idx1) == len(d2d1) == len(d3d1) == 20
def test_match_catalog_nounit():
from .. import ICRS, CartesianRepresentation
from ..matching import match_coordinates_sky
i1 = ICRS([[1], [2], [3]], representation_type=CartesianRepresentation)
i2 = ICRS([[1], [2], [4, 5]], representation_type=CartesianRepresentation)
i, sep, sep3d = match_coordinates_sky(i1, i2)
assert_allclose(sep3d, [1] * u.dimensionless_unscaled)
def report_close_neighbours(magmo_coords, magmo_table):
idx, sep2d, dist3d = matching.match_coordinates_sky(magmo_coords, magmo_coords, 2)
idx_orig = np.arange(len(magmo_coords))
idx_close = sep2d < 3 * u.arcsec
idx_match1 = idx_orig[idx_close]
idx_match2 = idx[idx_close]
sep_12 = sep2d[idx_close]
for i in range(len(idx_match1)):
match1 = magmo_table[idx_match1[i]]
match2 = magmo_table[idx_match2[i]]
print("{} is only {:.2f} arcsec from {} Rating {} v {} ContSD {:.3f} v {:.3f}".format(match1['Name'],
sep_12[i].to(u.arcsec),
match2['Name'], match1['Rating'],
match2['Rating'],
match1['Continuum_SD'],
match2['Continuum_SD']))
def test_matching_function_3d_and_sky():
from astropy.coordinates import ICRS
from astropy.coordinates.matching import match_coordinates_3d, match_coordinates_sky
cmatch = ICRS([4, 2.1]*u.degree, [0, 0]*u.degree, distance=[1, 5] * u.kpc)
ccatalog = ICRS([1, 2, 3, 4]*u.degree, [0, 0, 0, 0]*u.degree, distance=[1, 1, 1, 5] * u.kpc)
idx, d2d, d3d = match_coordinates_3d(cmatch, ccatalog)
npt.assert_array_equal(idx, [2, 3])
assert_allclose(d2d, [1, 1.9] * u.deg)
assert np.abs(d3d[0].to_value(u.kpc) - np.radians(1)) < 1e-6
assert np.abs(d3d[1].to_value(u.kpc) - 5*np.radians(1.9)) < 1e-5
idx, d2d, d3d = match_coordinates_sky(cmatch, ccatalog)
npt.assert_array_equal(idx, [3, 1])
assert_allclose(d2d, [0, 0.1] * u.deg)
assert_allclose(d3d, [4, 4.0000019] * u.kpc)
dec = rr_list['DEC'][k] print 'RA = %.10f, DEC= %.10f' % (ra,dec) row_pix = None col_pix = None dist = [] for CCD in CHIPS: print 'Looking in CCD %s' % CCD cata_file = "%s/catalogues/%s/%s/%s_%s_%s_image_crblaster_thresh%s_minarea%s_backsize64_trans.dat" % (jorgepath, field, CCD, field, CCD, '02', str(thresh), str(minarea)) if not os.path.exists(cata_file): print 'No file: %s' % files_epoch continue cata = np.loadtxt(cata_file, comments='#') tree = SkyCoord(ra = cata[:,3], dec = cata[:,4], unit=(u.degree,u.degree)) radec_obj = SkyCoord(ra = ra, dec = dec, unit=(u.degree,u.degree)) idx, ang, d3d = match_coordinates_sky(radec_obj, tree, nthneighbor = 1) ang0 = np.array(ang) dist.append(ang0) print 'Nearest neighbor at %f arcsec' % (ang0*3600) # ang0 is in degrees 0.0003 deg ~ 1.0 arcsec if ang0 < tolerance: row_pix, col_pix = cata[idx,2], cata[idx,1] print 'RADEC in catalogue: %f, %f' % (cata[idx,3], cata[idx,4]) print 'Angular distance: %f acrsec' % (ang0*3600) print 'YES!!' print '########################################' break else: print 'No match!' print '----------------------------------------'
def associate_object_ids_to_table(cat,
object_table=None,
matching_radius=1,
verbose=True):
"""Return object ID associated with each entry in cat.
Takes closest match within matching radius.
Entries with no match will be -1. (the index entries are integers so we can't use NaN as signaling value)
Currently does purely position based matching above an implicit SNR
Does not do a flux-based matching.
Parameters
--
cat: pandas DataFrame
object_table: pandas DataFrame
matching_radius: float [arcsec]
Notes
--
cat and object_table are assumed to have RA, Dec units of rad
"""
# Let's first try the stupid way:
# Explicitly iterate through cat
# Construct a subset of object
min_ra, max_ra = np.min(cat['coord_ra']), np.max(cat['coord_ra'])
min_dec, max_dec = np.min(cat['coord_dec']), np.max(cat['coord_dec'])
edge_buffer = np.deg2rad(10 * matching_radius / 3600)
# Note this is wrong. There's no cos(dec)
ra_range = (np.rad2deg(min_ra - edge_buffer),
np.rad2deg(max_ra + edge_buffer))
dec_range = (np.rad2deg(min_dec - edge_buffer),
np.rad2deg(max_dec + edge_buffer))
if verbose:
print("Extract objects from Object Table with RA, Dec in ranges:")
print(ra_range, dec_range)
in_source_catalog_area = (ra_range[0] < object_table['ra']) & \
(object_table['ra'] < ra_range[1]) & \
(dec_range[0] < object_table['dec']) & \
(object_table['dec'] < dec_range[1])
this_object_table = object_table[in_source_catalog_area]
if verbose:
print("Found %d objects in Object Table in region" %
len(this_object_table))
if len(this_object_table) < 1:
# Return an array of -1
return np.zeros(len(cat)) - 1
this_object_table_skyCoor = SkyCoord(this_object_table['ra'],
this_object_table['dec'],
unit=(u.deg, u.deg))
cat_skyCoor = SkyCoord(cat['coord_ra'],
cat['coord_dec'],
unit=(u.rad, u.rad))
idx, sep2d, _ = matching.match_coordinates_sky(cat_skyCoor,
this_object_table_skyCoor)
associated_ids = np.asarray(this_object_table.iloc[idx]['id'], dtype=int)
# Remove the associations that were too far away.
w = sep2d > matching_radius * u.arcsec
# sentinel value for no match.
# Assumes input catalog uses only non-negative numbers for object Ids
associated_ids[w] = -1
return associated_ids
for CCD in CHIPS:
print 'Looking in CCD %s' % CCD
cata_file = "%s/catalogues/%s/%s/%s_%s_%s_image_crblaster_thresh%s_minarea%s_backsize64_trans.dat" % (
jorgepath, field, CCD, field, CCD, '02', str(thresh),
str(minarea))
if not os.path.exists(cata_file):
print 'No file: %s' % files_epoch
continue
cata = np.loadtxt(cata_file, comments='#')
tree = SkyCoord(ra=cata[:, 3],
dec=cata[:, 4],
unit=(u.degree, u.degree))
radec_obj = SkyCoord(ra=ra, dec=dec, unit=(u.degree, u.degree))
idx, ang, d3d = match_coordinates_sky(radec_obj,
tree,
nthneighbor=1)
ang0 = np.array(ang)
dist.append(ang0)
print 'Nearest neighbor at %f arcsec' % (ang0 * 3600)
# ang0 is in degrees 0.0003 deg ~ 1.0 arcsec
if ang0 < tolerance:
row_pix, col_pix = cata[idx, 2], cata[idx, 1]
print 'RADEC in catalogue: %f, %f' % (cata[idx, 3], cata[idx,
4])
print 'Angular distance: %f acrsec' % (ang0 * 3600)
print 'YES!!'
print '########################################'
break
else:
def create_clusters(LSM, patches_orig, Q, applyBeam=False, root='Patch'):
"""
Clusterize all the patches of the skymodel iteratively around the brightest patches
"""
from astropy.coordinates import SkyCoord
from astropy import units as u
from distutils.version import StrictVersion
import scipy
log = logging.getLogger('LSMTool.Cluster')
if StrictVersion(scipy.__version__) < StrictVersion('0.11.0'):
log.debug('The installed version of SciPy contains a bug that affects catalog matching. '
'Falling back on (slower) matching script.')
from ._matching import match_coordinates_sky
else:
from astropy.coordinates.matching import match_coordinates_sky
# sort the patches by brightest first
idx = np.argsort([patch.flux for patch in patches_orig])[::-1] # -1 to reverse sort
patches = list(np.array(patches_orig)[idx])
# initialize clusters with the brightest patches
clusters = []
for i, patch in enumerate(patches[0:Q]):
clusters.append(Cluster(root+'_'+str(i), patch))
# Iterate until no changes in which patch belongs to which cluster
count = 1
patches_seq_old = []
patchRAs = []
patchDecs = []
for patch in patches:
patchRAs.append(patch.ra)
patchDecs.append(patch.dec)
while True:
clusterRAs = []
clusterDecs = []
if LSM.hasPatches:
clusterRA, clusterDec = LSM.getPatchPositions(method='wmean',
asArray=True, applyBeam=applyBeam, perPatchProjection=False)
clusterNames = LSM.getPatchNames()
patches_orig = LSM.getColValues('Name')
else:
clusterRA = [cluster.centroid_ra for cluster in clusters]
clusterDec = [cluster.centroid_dec for cluster in clusters]
clusterNames = [cluster.name for cluster in clusters]
for cluster in clusters:
# reset patches
if type(clusterNames) is not list:
clusterNames = clusterNames.tolist()
cindx = clusterNames.index(cluster.name)
cluster.patches = []
clusterRAs.append(clusterRA[cindx])
clusterDecs.append(clusterDec[cindx])
catalog1 = SkyCoord(clusterRAs, clusterDecs,
unit=(u.degree, u.degree), frame='fk5')
catalog2 = SkyCoord(patchRAs, patchDecs,
unit=(u.degree, u.degree), frame='fk5')
matchIdx, d2d, d3d = match_coordinates_sky(catalog2, catalog1)
for i, patch in zip(matchIdx, patches):
cluster = clusters[i]
cluster.add_patch(patch)
patches_seq = []
for cluster in clusters:
patches_seq.extend(cluster.patches)
count += 1
if patches_seq == patches_seq_old:
break
patches_seq_old = patches_seq
# Make output patch column
patchNames = [''] * len(patches)
patchNames_orig = LSM.getColValues('Name').tolist()
for c in clusters:
for p in c.patches:
patchNames[patchNames_orig.index(p.name)] = c.name
LSM.setColValues('Patch', patchNames, index=2)
LSM._updateGroups()
return np.array(patchNames)
##arcsec
match=[[] for i in range(len(seq1))]
for i in range(len(seq1)):
for j in range(len(seq2)):
dis=getlen([ra_dec2[0][j],ra_dec2[1][j]],[ra_dec1[0][i],ra_dec1[1][i]])*3600
if dis<offset:
match[i].append([seq1[i],seq2[j],dis])
print(np.sort(match))
input=get_input()
Radio_catalog = SkyCoord(ra=input[0][0]*u.degree, dec=input[0][1]*u.degree)
Xray_catalog = SkyCoord(ra=input[1][0]*u.degree, dec=input[1][1]*u.degree)
print(Xray_catalog)
id=match_catalog.match_coordinates_sky(Radio_catalog, Xray_catalog, nthneighbor=1)[0]+1
offset=match_catalog.match_coordinates_sky(Radio_catalog, Xray_catalog, nthneighbor=1)[1]
offset_arcsec=[]
for i in range(len(offset)):
offset_arcsec.append(Angle(offset[i]).degree*3600)
# temp=SkyCoord(offset[0], unit = (u.deg), frame='icrs').to_string('decimal')
xray_counterpart=np.column_stack((id.astype(int),offset_arcsec))
np.savetxt('/Users/baotong/Desktop/period/GCCR_xrayc.txt',xray_counterpart,fmt='get_ipython().run_line_magic("d", " %10.4f')")
def create_clusters(LSM, patches_orig, Q, applyBeam=False, root='Patch', pad_index=False):
"""
Clusterize all the patches of the skymodel iteratively around the brightest patches
"""
from astropy.coordinates import SkyCoord
from astropy import units as u
from distutils.version import StrictVersion
import scipy
log = logging.getLogger('LSMTool.Cluster')
if StrictVersion(scipy.__version__) < StrictVersion('0.11.0'):
log.debug('The installed version of SciPy contains a bug that affects catalog matching. '
'Falling back on (slower) matching script.')
from ._matching import match_coordinates_sky
else:
from astropy.coordinates.matching import match_coordinates_sky
# sort the patches by brightest first
idx = np.argsort([patch.flux for patch in patches_orig])[::-1] # -1 to reverse sort
patches = list(np.array(patches_orig)[idx])
# initialize clusters with the brightest patches
clusters = []
for i, patch in enumerate(patches[0:Q]):
if pad_index:
clusters.append(Cluster(root+'_'+str(i).zfill(int(np.ceil(np.log10(Q)))), patch))
else:
clusters.append(Cluster(root+'_'+str(i), patch))
# Iterate until no changes in which patch belongs to which cluster
count = 1
patches_seq_old = []
patchRAs = []
patchDecs = []
for patch in patches:
patchRAs.append(patch.ra)
patchDecs.append(patch.dec)
while True:
clusterRAs = []
clusterDecs = []
if LSM.hasPatches:
clusterRA, clusterDec = LSM.getPatchPositions(method='wmean',
asArray=True, applyBeam=applyBeam, perPatchProjection=False)
clusterNames = LSM.getPatchNames()
patches_orig = LSM.getColValues('Name')
else:
clusterRA = [cluster.centroid_ra for cluster in clusters]
clusterDec = [cluster.centroid_dec for cluster in clusters]
clusterNames = [cluster.name for cluster in clusters]
for cluster in clusters:
# reset patches
if type(clusterNames) is not list:
clusterNames = clusterNames.tolist()
cindx = clusterNames.index(cluster.name)
cluster.patches = []
clusterRAs.append(clusterRA[cindx])
clusterDecs.append(clusterDec[cindx])
catalog1 = SkyCoord(clusterRAs, clusterDecs,
unit=(u.degree, u.degree), frame='fk5')
catalog2 = SkyCoord(patchRAs, patchDecs,
unit=(u.degree, u.degree), frame='fk5')
matchIdx, d2d, d3d = match_coordinates_sky(catalog2, catalog1)
for i, patch in zip(matchIdx, patches):
cluster = clusters[i]
cluster.add_patch(patch)
patches_seq = []
for cluster in clusters:
patches_seq.extend(cluster.patches)
count += 1
if patches_seq == patches_seq_old:
break
patches_seq_old = patches_seq
# Make output patch column
patchNames = [''] * len(patches)
patchNames_orig = LSM.getColValues('Name').tolist()
for c in clusters:
for p in c.patches:
patchNames[patchNames_orig.index(p.name)] = c.name
LSM.setColValues('Patch', patchNames, index=2)
LSM._updateGroups()
return np.array(patchNames)
