def nearest_source(band, skypos, radius=0.01, maglimit=20.0, verbose=0, catalog="MCAT", retries=20):
"""Return targeting parameters for the nearest MCAT source to a position.
"""
out = np.array(
gQuery.getArray(
gQuery.mcat_sources(band, skypos[0], skypos[1], radius, maglimit=maglimit), verbose=verbose, retries=retries
)
)
if not len(out) and band == "FUV":
if verbose:
print "No nearby MCAT source found in FUV. Trying NUV..."
band = "NUV"
out = np.array(
gQuery.getArray(
gQuery.mcat_sources(band, skypos[0], skypos[1], radius, maglimit=maglimit),
verbose=verbose,
retries=retries,
)
)
if not len(out) and band == "NUV":
if verbose:
print "No nearby MCAT source found. Using input sky position."
return skypos[0], skypos[1], 0.01
# dist=np.sqrt( (out[:,0]-skypos[0])**2 + (out[:,1]-skypos[1])**2)
dist = angularSeparation(out[:, 0], out[:, 1], skypos[0], skypos[1])
if verbose > 1:
print "Finding nearest among " + str(len(dist)) + " nearby sources."
# Note that this doesn't cope with multiple entries for the same source.
s = out[np.where(dist == dist.min())][0]
# RA, Dec, NUV mag, FUV mag, NUV fwhm, FUV fwhm
return avg_sources(band, [s[0], s[1]], verbose=verbose, retries=retries)
def nearest_distinct_source(band,skypos,radius=0.1,maglimit=20.0,verbose=0,
catalog='MCAT',retries=20):
"""Return parameters for the nearest non-targeted source."""
out = np.array(gQuery.getArray(gQuery.mcat_sources(band,skypos[0],skypos[1],radius,maglimit=maglimit),verbose=verbose,retries=retries))
#dist=np.sqrt( (out[:,0]-skypos[0])**2 + (out[:,1]-skypos[1])**2)
dist = angularSeparation(out[:,0],out[:,1],skypos[0],skypos[1])
ix = np.where(dist>0.005)
return np.array(out)[ix][np.where(dist[ix]==dist[ix].min())][0]
def suggest_bg_radius(band, skypos, radius=0.1, maglimit=20.0, verbose=0, catalog="MCAT", retries=20):
"""Returns a recommended background radius based upon the
positions and FWHM of nearby sources in the MCAT.
"""
nearest = nearest_distinct_source(band, skypos, verbose=verbose, retries=retries)
# dist = np.sqrt( (nearest[0]-skypos[0])**2 + (nearest[1]-skypos[1])**2 )
dist = angularSeparation(nearest[0], nearest[1], skypos[0], skypos[1])
return round(dist - 3 * nearest[-2 if band == "NUV" else -1], 4)
def parse_unique_sources(ras,decs,fmags,nmags,margin=0.005):
"""Iteratively returns unique sources based upon a _margin_ within
which two sources should be considered the same sources. Is a little
bit sensitive to the first entry and could probably be written to be
more robust, but works well enough.
"""
skypos = zip(ras,decs)
for i,pos in enumerate(skypos):
ix = np.where(angularSeparation(pos[0],pos[1],ras,decs)<=margin)
skypos[i]=[ras[ix].mean(),decs[ix].mean()]
a = skypos #unique_sources(data['ra'],data['dec'])
b = []
for i in a:
if not (i in b):
b+=[i]
return b
def get_aspect(band,skypos,trange=[6e8,11e8],verbose=0,
detsize=1.25):
"""Get aspect solution in a dict() for given time range."""
asp = np.array(gQuery.getArray(gQuery.aspect_skypos(skypos[0],skypos[1],
detsize=detsize),verbose=verbose))
data = {'eclipse':np.array(asp[:,0],dtype='int16'),'filename':asp[:,1],
't':np.array(asp[:,2],dtype='float64')/tscale,
'ra':np.array(asp[:,3],dtype='float64'),
'dec':np.array(asp[:,4],dtype='float64'),
'twist':np.array(asp[:,5],dtype='float64'),
'flag':np.array(asp[:,6],dtype='int8'),
'ra0':np.array(asp[:,7],dtype='float64'),
'dec0':np.array(asp[:,8],dtype='float64'),
'twist0':np.array(asp[:,9],dtype='float64')}
ix = np.where((data['t']>trange[0]) & (data['t']<trange[1]) &
(angularSeparation(skypos[0],skypos[1],
data['ra'],data['dec'])<=detsize/2.))
for key in data.keys():
data[key] = data[key][ix]
return data
def get_aspect(band, skypos, trange=[6e8, 11e8], verbose=0, detsize=1.25):
"""Get aspect solution in a dict() for given time range."""
asp = np.array(gQuery.getArray(gQuery.aspect_skypos(skypos[0], skypos[1], detsize=detsize), verbose=verbose))
data = {
"eclipse": np.array(asp[:, 0], dtype="int16"),
"filename": asp[:, 1],
"t": np.array(asp[:, 2], dtype="float64") / tscale,
"ra": np.array(asp[:, 3], dtype="float64"),
"dec": np.array(asp[:, 4], dtype="float64"),
"twist": np.array(asp[:, 5], dtype="float64"),
"flag": np.array(asp[:, 6], dtype="int8"),
"ra0": np.array(asp[:, 7], dtype="float64"),
"dec0": np.array(asp[:, 8], dtype="float64"),
"twist0": np.array(asp[:, 9], dtype="float64"),
}
ix = np.where(
(data["t"] > trange[0])
& (data["t"] < trange[1])
& (angularSeparation(skypos[0], skypos[1], data["ra"], data["dec"]) <= detsize / 2.0)
)
for key in data.keys():
data[key] = data[key][ix]
return data
