def subpixelPeak(self, newimage=None, npix=5, guess=None, limit=None):
'''
see pixelPeak doc string for info about guess and limit
'''
if newimage is not None:
self.setImage(newimage)
if self.results['subpixel peak'] is not None:
return self.results['subpixel peak']
self.pixelPeak(guess=guess, limit=limit)
peakrow, peakcol = self.results['pixel peak']
## cut out a region of interest around the peak
roi = imagefun.crop_at(self.image, (peakrow, peakcol), (npix, npix))
## fit a quadratic to it and find the subpixel peak
roipeak = self.quadFitPeak(roi)
#roipeak = self.gaussFitPeak(roi)
subfailed = False
if roipeak['row'] < 0 or roipeak['row'] > npix or numpy.isnan(
roipeak['row']):
srow = float(peakrow)
subfailed = True
else:
srow = peakrow + roipeak['row'] - npix / 2
if roipeak['col'] < 0 or roipeak['col'] > npix or numpy.isnan(
roipeak['col']):
scol = float(peakcol)
subfailed = True
else:
scol = peakcol + roipeak['col'] - npix / 2
peakvalue = roipeak['value']
peakminsum = roipeak['minsum']
subpixelpeak = (srow, scol)
self.results['subpixel peak'] = subpixelpeak
self.results['subpixel peak value'] = peakvalue
self.results['minsum'] = peakminsum
self.results['coeffs'] = roipeak['coeffs']
self.results['subfailed'] = subfailed
#NEIL's SNR calculation
self.results['noise'] = nd_image.standard_deviation(self.image)
self.results['mean'] = nd_image.mean(self.image)
self.results[
'signal'] = self.results['pixel peak value'] - self.results['mean']
if self.results['noise'] == self.results[
'noise'] and self.results['noise'] != 0.0:
self.results[
'snr'] = self.results['signal'] / self.results['noise']
else:
self.results['snr'] = self.results['pixel peak value']
return subpixelpeak
def subpixelPeak(self, newimage=None, npix=5, guess=None, limit=None): ''' see pixelPeak doc string for info about guess and limit ''' if newimage is not None: self.setImage(newimage) if self.results['subpixel peak'] is not None: return self.results['subpixel peak'] self.pixelPeak(guess=guess, limit=limit) peakrow,peakcol = self.results['pixel peak'] ## cut out a region of interest around the peak roi = imagefun.crop_at(self.image, (peakrow,peakcol), (npix,npix)) ## fit a quadratic to it and find the subpixel peak roipeak = self.quadFitPeak(roi) #roipeak = self.gaussFitPeak(roi) subfailed = False if roipeak['row'] < 0 or roipeak['row'] > npix or numpy.isnan(roipeak['row']): srow = float(peakrow) subfailed = True else: srow = peakrow + roipeak['row'] - npix/2 if roipeak['col'] < 0 or roipeak['col'] > npix or numpy.isnan(roipeak['col']): scol = float(peakcol) subfailed = True else: scol = peakcol + roipeak['col'] - npix/2 peakvalue = roipeak['value'] peakminsum = roipeak['minsum'] subpixelpeak = (srow, scol) self.results['subpixel peak'] = subpixelpeak self.results['subpixel peak value'] = peakvalue self.results['minsum'] = peakminsum self.results['coeffs'] = roipeak['coeffs'] self.results['subfailed'] = subfailed #NEIL's SNR calculation self.results['noise'] = nd_image.standard_deviation(self.image) self.results['mean'] = nd_image.mean(self.image) self.results['signal'] = self.results['pixel peak value'] - self.results['mean'] if self.results['noise'] == self.results['noise'] and self.results['noise'] != 0.0: self.results['snr'] = self.results['signal'] / self.results['noise'] else: self.results['snr'] = self.results['pixel peak value'] return subpixelpeak
def pixelPeak(self, newimage=None, guess=None, limit=None): """ guess = where to center your search for the peak (row,col) limit = shape of the search box (with guess at the center) Setting guess and limit can serve two purposes: 1) You can imit your peak search if you are pretty sure where it will be 2) Given that the image may wrap around into negative space, you can specify that you want to search for the peak in these out of bounds areas. For instance, a (512,512) image may have a peak at (500,500). You may specify a guess of (-10,-10) and a relatively small limit box. The (500,500) peak will be found, but it will be returned as (-12,-12). """ if newimage is not None: self.setImage(newimage) if self.results['pixel peak'] is None: if None not in (guess, limit): cropcenter = limit[0]/2.0-0.5, limit[1]/2.0-0.5 im = imagefun.crop_at(self.image, guess, limit) else: cropcenter = None im = self.image peak = numpy.argmax(im.ravel()) peakvalue = im.flat[peak] rows,cols = im.shape peakrow = peak / cols peakcol = peak % cols if cropcenter is not None: peakrow = int(round(guess[0]+peakrow-cropcenter[0])) peakcol = int(round(guess[1]+peakcol-cropcenter[1])) pixelpeak = (peakrow, peakcol) self.results['pixel peak'] = pixelpeak self.results['pixel peak value'] = peakvalue if peakrow < 0: unsignedr = peakrow + self.image.shape[0] else: unsignedr = peakrow if peakcol < 0: unsignedc = peakcol + self.image.shape[0] else: unsignedc = peakcol self.results['unsigned pixel peak'] = unsignedr,unsignedc #NEIL's SNR calculation self.results['noise'] = nd_image.standard_deviation(im) self.results['mean'] = nd_image.mean(im) self.results['signal'] = self.results['pixel peak value'] - self.results['mean'] if self.results['noise'] == self.results['noise'] and self.results['noise'] != 0.0: self.results['snr'] = self.results['signal'] / self.results['noise'] else: self.results['snr'] = self.results['pixel peak value'] #print self.results['noise'],self.results['mean'],self.results['signal'],self.results['snr'] return self.results['pixel peak']
def pixelPeak(self, newimage=None, guess=None, limit=None):
"""
guess = where to center your search for the peak (row,col)
limit = shape of the search box (with guess at the center)
Setting guess and limit can serve two purposes:
1) You can imit your peak search if you are pretty sure
where it will be
2) Given that the image may wrap around into negative
space, you can specify that you want to search for the peak
in these out of bounds areas. For instance, a (512,512)
image may have a peak at (500,500). You may specify a guess
of (-10,-10) and a relatively small limit box.
The (500,500) peak will be found, but it will be returned
as (-12,-12).
"""
if newimage is not None:
self.setImage(newimage)
if self.results['pixel peak'] is None:
if None not in (guess, limit):
cropcenter = limit[0] / 2.0 - 0.5, limit[1] / 2.0 - 0.5
im = imagefun.crop_at(self.image, guess, limit)
else:
cropcenter = None
im = self.image
peak = numpy.argmax(im.ravel())
peakvalue = im.flat[peak]
rows, cols = im.shape
peakrow = peak / cols
peakcol = peak % cols
if cropcenter is not None:
peakrow = int(round(guess[0] + peakrow - cropcenter[0]))
peakcol = int(round(guess[1] + peakcol - cropcenter[1]))
pixelpeak = (peakrow, peakcol)
self.results['pixel peak'] = pixelpeak
self.results['pixel peak value'] = peakvalue
if peakrow < 0:
unsignedr = peakrow + self.image.shape[0]
else:
unsignedr = peakrow
if peakcol < 0:
unsignedc = peakcol + self.image.shape[0]
else:
unsignedc = peakcol
self.results['unsigned pixel peak'] = unsignedr, unsignedc
#NEIL's SNR calculation
self.results['noise'] = nd_image.standard_deviation(im)
self.results['mean'] = nd_image.mean(im)
self.results['signal'] = self.results[
'pixel peak value'] - self.results['mean']
if self.results['noise'] == self.results[
'noise'] and self.results['noise'] != 0.0:
self.results[
'snr'] = self.results['signal'] / self.results['noise']
else:
self.results['snr'] = self.results['pixel peak value']
#print self.results['noise'],self.results['mean'],self.results['signal'],self.results['snr']
return self.results['pixel peak']