def test_exposure_ranges(self):
query = (str(self.baseURL)+
'select vpo.objid, ra, dec, nuv_mag, fuv_mag, fov_radius, nuv_skybg,'
' fuv_skybg, nuv_fwhm_world, fuv_fwhm_world, vpe.fexptime,'
' vpe.nexptime, fuv_mag_aper_1, fuv_mag_aper_2, fuv_mag_aper_3,'
' fuv_mag_aper_4, fuv_mag_aper_5, fuv_mag_aper_6, fuv_mag_aper_7,'
' nuv_mag_aper_1, nuv_mag_aper_2, nuv_mag_aper_3, nuv_mag_aper_4,'
' nuv_mag_aper_5, nuv_mag_aper_6, nuv_mag_aper_7,'
' fuv_magerr_aper_1, fuv_magerr_aper_2, fuv_magerr_aper_3,'
' fuv_magerr_aper_4, fuv_magerr_aper_5, fuv_magerr_aper_6,'
' fuv_magerr_aper_7, nuv_magerr_aper_1, nuv_magerr_aper_2,'
' nuv_magerr_aper_3, nuv_magerr_aper_4, nuv_magerr_aper_5,'
' nuv_magerr_aper_6, nuv_magerr_aper_7'
' from '+str(self.MCATDB)+'.visitphotoobjall as vpo'
' inner join '+str(self.MCATDB)+'.visitphotoextract'
' as vpe on vpo.photoextractid=vpe.photoextractid inner join'
' '+str(self.MCATDB)+'.fGetNearbyVisitObjEq('+repr(float(self.ra0))+','+
repr(float(self.dec0))+', '+str(self.radius*60.)+
') as nb on vpo.objid=nb.objid'+str(self.formatURL))
self.assertEqual(gq.exposure_ranges(self.NUV,self.ra0,self.dec0,t0=self.t0,t1=self.t1,detsize=self.detsize),query)
def get_valid_times(band,skypos,trange=None,detsize=1.1,verbose=0,retries=100.,
skyrange=None):
if not np.array(trange).tolist():
trange = [1,1000000000000]
if len(np.shape(trange))==2:
trange=trange[0]
# FIXME: This is probably not an optimally efficient way to check an entire
# region of sky for data, but it's not hugely dumb and does work...
# Assemble sky positions on a grid within the targeted region.
skypos_list = [skypos]
if skyrange:
for r in np.linspace(skypos[0]-skyrange[0]/2.,skypos[0]+skyrange[0]/2.,
np.ceil(skyrange[0]/(detsize/2.)),endpoint=True):
for d in np.linspace(skypos[1]-skyrange[1]/2.,
skypos[1]+skyrange[1]/2.,np.ceil(skyrange[1]/(detsize/2.)),
endpoint=True):
skypos_list += [[r,d]]
times = []
for skypos in skypos_list:
try:
times = (list(times) +
list(np.array(gQuery.getArray(gQuery.exposure_ranges(
band,skypos[0],skypos[1],t0=trange[0],t1=trange[1],
detsize=detsize),verbose=verbose,retries=retries),
dtype='float64')[:,0]/tscale))
except IndexError:
if verbose:
print "No exposure time available at {pos}".format(pos=skypos)
return np.array([],dtype='float64')
except TypeError:
print "Is one of the inputs malformed?"
raise
except:
raise
return np.sort(np.unique(times))
def test_exposure_ranges(self):
self.assertEqual(gq.exposure_ranges(self.NUV,self.ra0,self.dec0,t0=self.t0,t1=self.t1,detsize=self.detsize),"http://masttest.stsci.edu/portal/Mashup/MashupQuery.asmx/GalexPhotonListQueryTest?query=select distinct time from fGetNearbyAspectEq(176.919525856,0.255696872807,((1.25/2.0)*60.0),766525332995,866526576995) where band='NUV' or band='FUV/NUV' order by time&format=json&timeout={}")
def fGetTimeRanges(band,skypos,trange=None,tscale=1000.,detsize=1.25,verbose=0,
maxgap=1.,minexp=1.,retries=100.,predicted=False):
"""Find the contiguous time ranges within a time range at a
specific location.
minexp - Do not include exposure time less than this.
maxgap - Gaps in exposure longer than this initiate a new time range.
detsize - Fiddle with this if you want to exlude the edges of the
detector.
predicted - Use the aspect solutions to estimate what exposure will be
available once the database is fully populated.
"""
try:
if not np.array(trange).tolist():
trange = [1,1000000000000]
if len(np.shape(trange))==2:
trange=trange[0]
times = (np.array(gQuery.getArray(gQuery.exposure_ranges(band,
skypos[0],skypos[1],t0=trange[0],t1=trange[1],detsize=detsize,
tscale=tscale),verbose=verbose,retries=retries),
dtype='float64')[:,0]/tscale
if not predicted else get_aspect(band,skypos,trange,
tscale=tscale,verbose=verbose)['t'])
except IndexError:
if verbose:
print "No exposure time available at {pos}".format(pos=skypos)
return np.array([],dtype='float64')
except TypeError:
print "Is one of the inputs malformed?"
raise
except:
raise
if verbose:
print_inline('Parsing '+str(len(times)-1)+' seconds of exposure.: ['+str(trange[0])+', '+str(trange[1])+']')
blah = []
for i in xrange(len(times[0:-1])):
blah.append(times[i+1]-times[i])
# A drop in data with duration greater than maxgap initiates a
# new exposure
gaps = np.where(np.array(blah)>maxgap)
ngaps = len(gaps[0])
chunks = []
for i in range(ngaps):
if not i:
chunk = [times[0],times[gaps[0][i]]]
elif i==ngaps-1:
chunk = [times[gaps[0][i]+1],times[-1]]
else:
chunk = [times[gaps[0][i]+1],times[gaps[0][i+1]]]
# If the duration of this slice is less than minexp, do not
# count it as valid exposure.
if chunk[1]-chunk[0]<minexp:
continue
else:
chunks.append(chunk)
if not ngaps:
if times.min()==times.max():
chunks.append([times.min(),times.min()+1])
else:
chunks.append([times.min(),times.max()])
return np.array(chunks,dtype='float64')
