def scale_se_image(im,
exptime=NOMINAL_EXPTIME,
scale=SCALE,
nonlinear=NONLINEAR,
nominal_exptime=NOMINAL_EXPTIME):
"""
The median sky is subtracted separately for each amplifier.
An asinh stretch is applied.
The default nonlinear factor and scale are appropriate for an r-band
90 second exposure. Enter something different to scale appropriately.
"""
from numpy import median
import images
ims=im.astype('f4')
ims[:, 0:1024] = im[:, 0:1024] - median( im[:,0:1024] )
ims[:, 1024:] = im[:, 1024:] - median( im[:,1024:] )
ims *= (scale*nominal_exptime/exptime)
ims = images.asinh_scale(ims, nonlinear)
return ims
def view_peaks(*,
image,
noise,
objects,
show=False,
color='red',
type='filled circle',
width=800,
plt=None):
"""
view the image with peak positions overplotted
"""
import biggles
import images
tim = image.copy()
if plt is None:
aim = images.asinh_scale(image, noise=noise)
plt = images.view(aim, show=False)
# the viewer transposes the image
points = biggles.Points(
objects['col'],
objects['row'],
color=color,
type=type,
)
plt.add(points)
if show:
arat = image.shape[0] / image.shape[1]
plt.show(width=width, height=width * arat)
return plt
def scale_image_list(imlist, nonlinear):
import images
imslist=[]
for im in imlist:
ims=images.asinh_scale(im, nonlinear)
imslist.append(ims)
return imslist
def scale_se_image(im,
exptime=NOMINAL_EXPTIME,
scale=SCALE,
nonlinear=NONLINEAR,
nominal_exptime=NOMINAL_EXPTIME):
"""
An asinh stretch is applied.
The default nonlinear factor and scale are appropriate for an r-band
90 second exposure. Enter something different to scale appropriately.
"""
from numpy import median
import images
ims=im.copy()
ims *= (scale*nominal_exptime/exptime)
ims = images.asinh_scale(ims, nonlinear)
return ims
def scale_se_image(im,
exptime=NOMINAL_EXPTIME,
scale=SCALE,
nonlinear=NONLINEAR,
nominal_exptime=NOMINAL_EXPTIME):
"""
An asinh stretch is applied.
The default nonlinear factor and scale are appropriate for an r-band
90 second exposure. Enter something different to scale appropriately.
"""
from numpy import median
import images
ims = im.copy()
ims *= (scale * nominal_exptime / exptime)
ims = images.asinh_scale(ims, nonlinear)
return ims
