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