def loci_medfilter(self):
log = self.log
if self._file_exists('red','cube_loci_medfilter'): return
ft = lambda i: '%d:%.2d' % ((time.time()-i)/60,(time.time()-i)%60)
pa = self.pa
for chan in ['red','blue']:
cube = self._read_fits(chan,'cube_medfilter')
bp,cube = nt.reset_nans(cube)
log.info('Applying loci method ...(Can take a while......)')
ti = time.time()
cube = loci_subtract(cube, pa)
log.info(' '+ft(ti))
sz = np.shape(cube)
for i in range(sz[0]):
log.info('rotating cube_medfilter['+str(i+1)+'/'+str(sz[0])+'] %.2f'%-pa[i])
cube[i,:,:] = nd.rotate (cube[i,:,:],pa[i], reshape=False)
bp[i,:,:] = nd.rotate (bp[i,:,:],pa[i], reshape=False)
#cube[i,:,:] = nd.rotate (cube[i,:,:],-pa[i], reshape=False)
#bp[i,:,:] = nd.rotate (bp[i,:,:],-pa[i], reshape=False)
cube = nt.restore_nans(cube,bp)
self._write_fits(cube,chan,'cube_loci_medfilter')
self._write_fits(np.median(cube,axis=0),chan,'loci_medfilter_medcrunch')
fs = lambda cube: np.sum(cube,axis=0) / np.sum(np.isfinite(cube),axis=0)
self._write_fits(fs(cube),chan,'loci_medfilter_sumcrunch')
del cube,bp
def loci_sdi(self):
log=self.log
ft = lambda i: '%d:%.2d' % ((time.time()-i)/60,(time.time()-i)%60)
if self._file_exists('','cube_loci_sdi'): return
pa = self.pa
cube = self._read_fits('','cube_sdi')
#cube = self.cube_diff
sz = np.shape(cube)
bp,cube = nt.reset_nans(cube)
log.info('Applying loci_subtract...(Can take a while......)')
ti = time.time()
cube = loci_subtract(cube, self.pa)
#bp = loci_subtract(bp, self.pa)
log.info(' '+ft(ti))
for i in range(sz[0]):
log.info('rotating cube_diff['+str(i+1)+'/'+str(sz[0])+'] %.2f'%-pa[i])
cube[i,:,:] = nd.rotate (cube[i,:,:],pa[i], reshape=False)
bp[i,:,:] = nd.rotate (bp[i,:,:],pa[i], reshape=False)
#cube[i,:,:] = nd.rotate (cube[i,:,:],-pa[i], reshape=False)
#bp[i,:,:] = nd.rotate (bp[i,:,:],-pa[i], reshape=False)
cube = nt.restore_nans(cube,bp)
self._write_fits(cube,'','cube_loci_sdi')
self._write_fits(np.median(cube,axis=0),'','loci_sdi_medcrunch')
fs = lambda cube: np.sum(cube,axis=0) / np.sum(np.isfinite(cube),axis=0)
self._write_fits(fs(cube),'','loci_sdi_sumcrunch')
def _scale_im(self,im,scale):
def _scale_1024(out,scale):
x = (out[1]-512.)/scale + 512.
y = (out[0]-512.)/scale + 512.
return y,x
def _scale_1600(out,scale):
x = (out[1]-800.)/scale + 800.
y = (out[0]-800.)/scale + 800.
return y,x
def _scale_512(out, scale):
x = (out[1]-256.)/scale + 256.
y = (out[0]-256.)/scale + 256.
return y,x
im = np.asarray(im,dtype=np.float32)
bp,im = nt.reset_nans(im)
if (self.central):
im = nd.geometric_transform(im,_scale_512,extra_arguments=(scale,))
bp = nd.geometric_transform(bp,_scale_512,extra_arguments=(scale,))
else:
im = nd.geometric_transform(im,_scale_1024,extra_arguments=(scale,))
bp = nd.geometric_transform(bp,_scale_1024,extra_arguments=(scale,))
im = nt.restore_nans(im,bp)
return im
def _rotate_cube(self,cube):
log = self.log
pa = self.pa
sz = np.shape(cube)
# Now rotate this cube
to=time.time()
for k in range(sz[0]):
ti=time.time()
bp,cube[k,:,:] = nt.reset_nans(cube[k,:,:])
cube[k,:,:] = nd.rotate (cube[k,:,:], pa[k], reshape=False)
#cube[k,:,:] = nd.rotate (cube[k,:,:], -pa[k], reshape=False)
#Now rotate the bp matrix
#bp = nd.rotate (bp, -pa[k], reshape=False)
bp = nd.rotate (bp, pa[k], reshape=False)
# And restore the Nans to cube
cube[k,:,:] = nt.restore_nans(cube[k,:,:],bp)
#_form_line('rotating ,k,nk,to,t1,t2,pa[k])
t = time.time()
line = 'rotating ['+str(k+1)+'/'+str(sz[0])+']'
log.info(line+'%.2f %.2f %.2f' % (pa[k],t-ti,t-to))
return cube
if ext == 2:
# register blue frame to red's frame coordinate system
im = nd.geometric_transform (im,xy_tran)
#ndis.display(bp,frame=1,zscale=False)
bp = nd.geometric_transform (bp,xy_tran)
#ndis.display(bp,frame=2,zscale=False)
# Get the center of the mask automatically. If not possible
# nici_cntrd will display the image for the user to click
xcen,ycen,im = nc.nici_cntrd(im,hdr,center_im=True)
xco,yco,bp = nc.nici_cntrd(bp,hdr,center_im=True)
# Now restore the Nans
im = nt.restore_nans(im,bp)
#print 'NUMBER OF bp>1',np.sum(bp>1.)
#print 'NUMBER OF NANS',np.sum(np.isnan(im))
log.info('%s %s: xcen: %.2f ycen: %.2f' %
(outfile,
['red','blue'][ext-1],
xcen,ycen))
# Order wcs -in place.
nt.order_wcs(hdr)
hdr.update("EXTNAME",'SCI', "SCIENCE extension",after='GCOUNT')
hdr.__delitem__("EXTVER")
hdr.update("EXTVER",ext, "SCIENCE ext version",after='EXTNAME')
pf.append(outfile,im,hdr)
# gives the optimal (in a least-square sense)
# amplitude of the blue frame to fit
# the red frame. The blue frame is scaled to this
# optimal amplitude
if l2 < l1:
diff[:,:] = bim - rim
else:
diff[:,:] = rim - bim
bp,diff = nt.reset_nans(diff)
diff = nd.rotate (diff,angle, reshape=False)
bp = nd.rotate (bp,angle, reshape=False)
#diff = nd.rotate (diff,-angle, reshape=False)
#bp = nd.rotate (bp,-angle, reshape=False)
diff = nt.restore_nans(diff,bp)
return diff
def _medfilter(self,im,r,rad):
"""
Median filtering of each slice in the cube
"""
log = self.log
pa = self.pa
bsize = self.boxcar_size # Boxcar smoothing size (def:5)
mdfw = self.medfilt_width # Medfiltering width (def:11)
# to accelerate the code, we bin the image and the 'r'
# image down to 256x256 and do the radial profile on the binned versions