def test_compute_flat_scale_NUV_array(self):
band = 'NUV'
ts=[869777733.995,901049156.995]
scales=[0.986709143129,0.994262914909]
out = gt.compute_flat_scale(ts,band,verbose=0)
self.assertEqual(len(ts),len(out))
self.assertAlmostEqual(out[0],scales[0])
self.assertAlmostEqual(out[0],scales[0])
def test_compute_flat_scale_FUV_array(self):
band = 'FUV'
ts=[869777733.995,901049156.995]
scales=[0.991157347104,0.999816178202]
out = gt.compute_flat_scale(ts,band,verbose=0)
self.assertEqual(len(ts),len(out))
self.assertAlmostEqual(out[0],scales[0])
self.assertAlmostEqual(out[0],scales[0])
def hashresponse(band,events,verbose=0):
"""Given detector xi, eta, return the response at each position."""
# Hash out the response correction
if verbose:
mc.print_inline("Applying the response correction.")
flat, _ = cal.flat(band)
events['col'], events['row'] = xieta2colrow(
events['xi'], events['eta'], band)
events['flat'] = flat[np.array(events['col'], dtype='int16'),
np.array(events['row'], dtype='int16')]
events['scale'] = gxt.compute_flat_scale(events['t'], band)
# TODO: Separately do the binlinearly interpolated response
events['response'] = (events['flat']*events['scale'])
return events
def rrhr(band,skypos,tranges,skyrange,width=False,height=False,stepsz=1.,
verbose=0,calpath='../cal/',tscale=1000.,response=True,hdu=False,
retries=20):
"""Generate a high resolution relative response (rrhr) map."""
imsz = gxt.deg2pix(skypos,skyrange)
# TODO the if width / height
flat = get_fits_data(flat_filename(band,calpath),verbose=verbose)
flatinfo = get_fits_header(flat_filename(band,calpath))
npixx,npixy = flat.shape
fltsz = flat.shape
pixsz = flatinfo['CDELT2']
detsize = 1.25
# Rotate the flat into the correct orientation to start.
flat = np.flipud(np.rot90(flat))
# NOTE: This upsample interpolation is done _last_ in the canonical
# pipeline as part of the poissonbg.c routine.
# The interpolation function is "congrid" in the same file.
# TODO: Should this be first order interpolation? (i.e. bilinear)
hrflat = scipy.ndimage.interpolation.zoom(flat,4.,order=0,prefilter=False)
img = np.zeros(hrflat.shape)[
hrflat.shape[0]/2.-imsz[0]/2.:hrflat.shape[0]/2.+imsz[0]/2.,
hrflat.shape[1]/2.-imsz[1]/2.:hrflat.shape[1]/2+imsz[1]/2.]
for trange in tranges:
t0,t1=trange
entries = gQuery.getArray(gQuery.aspect(t0,t1),retries=retries)
n = len(entries)
asptime = np.float64(np.array(entries)[:,2])/tscale
aspra = np.float32(np.array(entries)[:,3])
aspdec = np.float32(np.array(entries)[:,4])
asptwist= np.float32(np.array(entries)[:,5])
aspflags= np.float32(np.array(entries)[:,6])
asptwist= np.float32(np.array(entries)[:,9])
aspra0 = np.zeros(n)+skypos[0]
aspdec0 = np.zeros(n)+skypos[1]
xi_vec, eta_vec = gnomonic.gnomfwd_simple(
aspra,aspdec,aspra0,aspdec0,-asptwist,1.0/36000.,0.)
col = 4.*( ((( xi_vec/36000.)/(detsize/2.)*(detsize/(fltsz[0]*pixsz)) + 1.)/2. * fltsz[0]) - (fltsz[0]/2.) )
row = 4.*( (((eta_vec/36000.)/(detsize/2.)*(detsize/(fltsz[1]*pixsz)) + 1.)/2. * fltsz[1]) - (fltsz[1]/2.) )
vectors = rotvec(np.array([col,row]),-asptwist)
for i in range(n):
if verbose>1:
print_inline('Stamping '+str(asptime[i]))
# FIXME: Clean this mess up a little just for clarity.
img += scipy.ndimage.interpolation.shift(scipy.ndimage.interpolation.rotate(hrflat,-asptwist[i],reshape=False,order=0,prefilter=False),[vectors[1,i],vectors[0,i]],order=0,prefilter=False)[hrflat.shape[0]/2.-imsz[0]/2.:hrflat.shape[0]/2.+imsz[0]/2.,hrflat.shape[1]/2.-imsz[1]/2.:hrflat.shape[1]/2+imsz[1]/2.]*dbt.compute_exptime(band,[asptime[i],asptime[i]+1],verbose=verbose,retries=retries)*gxt.compute_flat_scale(asptime[i]+0.5,band,verbose=0)
return img
def test_compute_flat_scale_NUV(self):
band = 'NUV'
ts=[869777733.995,901049156.995]
scales=[0.986709143129,0.994262914909]
for t,scl in zip(ts,scales):
self.assertAlmostEqual(gt.compute_flat_scale(t,band,verbose=0),scl)
def test_compute_flat_scale_FUV(self):
band = 'FUV'
ts=[869777733.995,901049156.995]
scales=[0.991157347104,0.999816178202]
for t,scl in zip(ts,scales):
self.assertAlmostEqual(gt.compute_flat_scale(t,band,verbose=0),scl)
