def fits_mean_file(files, outfile, overwrite=True, bitpix=32):
output = fits.HDUList()
output.append(fits.PrimaryHDU())
all_amps = allAmps()
for amp in all_amps:
images = [afwImage.ImageF(item, dm_hdu(amp)) for item in files]
if lsst.afw.__version__.startswith('12.0'):
images = afwImage.vectorImageF(images)
mean_image = afwMath.statisticsStack(images, afwMath.MEAN)
if bitpix < 0:
output.append(fits.ImageHDU(data=mean_image.getArray()))
else:
output.append(
fits.CompImageHDU(data=mean_image.getArray(),
compression_type='RICE_1'))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning, append=True)
warnings.filterwarnings('ignore', category=AstropyWarning, append=True)
warnings.filterwarnings('ignore',
category=AstropyUserWarning,
append=True)
with fits.open(files[0]) as template:
output[0].header.update(template[0].header)
output[0].header['FILENAME'] = os.path.basename(outfile)
for amp in all_amps:
output[amp].header.update(template[amp].header)
set_bitpix(output[amp], bitpix)
for i in (-3, -2, -1):
output.append(template[i])
fitsWriteto(output, outfile, overwrite=overwrite)
def stack(ims, statistic=afwMath.MEDIAN):
"""Stacks a list of images based on a statistic."""
images = []
for image in ims:
images.append(image)
if lsst.afw.__version__.startswith('12.0'):
images = afwImage.vectorImageF(images)
summary = afwMath.statisticsStack(images, statistic)
return summary
def main():
nImg = 10
nX, nY = 64, 64
imgList = afwImage.vectorImageF()
for iImg in range(nImg):
imgList.push_back(afwImage.ImageF(afwGeom.Extent2I(nX, nY), iImg))
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN)
print imgStack.get(nX / 2, nY / 2)
def main():
nImg = 10
nX, nY = 64, 64
imgList = afwImage.vectorImageF()
for iImg in range(nImg):
imgList.push_back(afwImage.ImageF(afwGeom.Extent2I(nX, nY), iImg))
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN)
print imgStack.get(nX/2, nY/2)
def testRequestMoreThanOneStat(self):
""" Make sure we throw an exception if someone requests more than one type of statistics. """
sctrl = afwMath.StatisticsControl()
imgList = afwImage.vectorImageF()
for val in self.values:
img = afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), val)
imgList.push_back(img)
def tst():
imgStackBad = afwMath.statisticsStack(imgList, afwMath.MEAN | afwMath.MEANCLIP, sctrl)
self.assertRaises(pexEx.InvalidParameterError, tst)
def testMean(self):
""" Test the statisticsStack() function for a MEAN"""
knownMean = 0.0
imgList = afwImage.vectorImageF()
for iImg in range(self.nImg):
imgList.push_back(afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), iImg))
knownMean += iImg
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN)
knownMean /= self.nImg
self.assertEqual(imgStack.get(self.nX/2, self.nY/2), knownMean)
def testRequestMoreThanOneStat(self):
""" Make sure we throw an exception if someone requests more than one type of statistics. """
sctrl = afwMath.StatisticsControl()
imgList = afwImage.vectorImageF()
for val in self.values:
img = afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), val)
imgList.push_back(img)
def tst():
imgStackBad = afwMath.statisticsStack(imgList, afwMath.MEAN | afwMath.MEANCLIP, sctrl)
utilsTests.assertRaisesLsstCpp(self, pexEx.InvalidParameterException, tst)
def testStatistics(self):
""" Test the statisticsStack() function """
imgList = afwImage.vectorImageF()
for val in self.values:
imgList.push_back(afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), val))
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN)
mean = reduce(lambda x, y: x+y, self.values)/float(len(self.values))
self.assertAlmostEqual(imgStack.get(self.nX//2, self.nY//2), mean)
imgStack = afwMath.statisticsStack(imgList, afwMath.MEDIAN)
median = sorted(self.values)[len(self.values)//2]
self.assertEqual(imgStack.get(self.nX//2, self.nY//2), median)
def testStatistics(self):
""" Test the statisticsStack() function """
imgList = afwImage.vectorImageF()
for val in self.values:
imgList.push_back(afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), val))
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN)
mean = reduce(lambda x, y: x+y, self.values)/float(len(self.values))
self.assertAlmostEqual(imgStack.get(self.nX/2, self.nY/2), mean)
imgStack = afwMath.statisticsStack(imgList, afwMath.MEDIAN)
median = sorted(self.values)[len(self.values)//2]
self.assertEqual(imgStack.get(self.nX/2, self.nY/2), median)
def testConstantWeightedStack(self):
""" Test statisticsStack() function when weighting by a vector of weights"""
sctrl = afwMath.StatisticsControl()
imgList = afwImage.vectorImageF()
weights = afwMath.vectorF()
for val in self.values:
img = afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), val)
imgList.push_back(img)
weights.push_back(val)
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN, sctrl, weights)
wsum = reduce(lambda x, y: x + y, self.values)
wvalues = [x*x for x in self.values]
wmean = reduce(lambda x, y: x + y, wvalues)/float(wsum)
self.assertAlmostEqual(imgStack.get(self.nX//2, self.nY//2), wmean)
def testMean(self):
""" Test the statisticsStack() function for a MEAN"""
knownMean = 0.0
imgList = afwImage.vectorImageF()
for iImg in range(self.nImg):
imgList.push_back(afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), iImg))
knownMean += iImg
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN)
knownMean /= self.nImg
self.assertEqual(imgStack.get(self.nX/2, self.nY/2), knownMean)
# Test in-place stacking
afwMath.statisticsStack(imgStack, imgList, afwMath.MEAN)
self.assertEqual(imgStack.get(self.nX/2, self.nY/2), knownMean)
def testConstantWeightedStack(self):
""" Test statisticsStack() function when weighting by a vector of weights"""
sctrl = afwMath.StatisticsControl()
imgList = afwImage.vectorImageF()
weights = afwMath.vectorF()
for val in self.values:
img = afwImage.ImageF(afwGeom.Extent2I(self.nX, self.nY), val)
imgList.push_back(img)
weights.push_back(val)
imgStack = afwMath.statisticsStack(imgList, afwMath.MEAN, sctrl, weights)
wsum = reduce(lambda x, y: x + y, self.values)
wvalues = [x*x for x in self.values]
wmean = reduce(lambda x, y: x + y, wvalues)/float(wsum)
self.assertAlmostEqual(imgStack.get(self.nX/2, self.nY/2), wmean)
def fits_median(files, hdu=2, fix=True):
"""Compute the median image from a set of image FITS files."""
ims = [afwImage.ImageF(f, hdu) for f in files]
exptimes = [Metadata(f).get('EXPTIME') for f in files]
if min(exptimes) != max(exptimes):
raise RuntimeError("Error: unequal exposure times")
if fix:
medians = np.array([median(im) for im in ims])
med = sum(medians) / len(medians)
errs = medians - med
for im, err in zip(ims, errs):
im -= err
if lsst.afw.__version__.startswith('12.0'):
ims = afwImage.vectorImageF(ims)
median_image = afwMath.statisticsStack(ims, afwMath.MEDIAN)
return median_image
def stack(self,
sensor_id,
infiles,
mask_files,
gains,
binsize=1,
bias_frame=None,
exps=['0.27', '0.54', '1.60', '2.40']):
for exp in exps:
all_amps = imutils.allAmps(infiles[0])
#ptc_stats = dict([amp, ([],[])) for amp in all_amps])
exposure = []
bitpix = None
overscan = makeAmplifierGeometry(infiles[0]).serial_overscan
mean_stack = fits.open(infiles[0])
var_stack = fits.open(infiles[0])
sum_stack = fits.open(infiles[0])
median_stack = fits.open(infiles[0])
for amp in imutils.allAmps(infiles[0]):
print 'on amp number', amp
images = afwImage.vectorImageF()
for idx, infile in enumerate(infiles):
print infile
ccd = MaskedCCD(infile,
mask_files=(),
bias_frame=bias_frame)
image = ccd.unbiased_and_trimmed_image(amp)
if idx == 0:
fratio_im = afwImage.MaskedImageF(image)
fratio_im = fratio_im.getImage().getArray()
image_array = image.getImage().getArray()
fratio = np.mean(
fratio_im[50:fratio_im.shape[0] - 50,
50:fratio_im.shape[1] - 50]) / np.mean(
image_array[50:image_array.shape[0] - 50,
50:image_array.shape[1] -
50])
image = afwImage.MaskedImageF(image).getImage()
image *= fratio
images.push_back(image)
meanstack_image = afwMath.statisticsStack(
images, afwMath.MEAN)
varstack_image = afwMath.statisticsStack(
images, afwMath.VARIANCE)
sumstack_image = afwMath.statisticsStack(
images, afwMath.SUM)
medianstack_image = afwMath.statisticsStack(
images, afwMath.MEDIAN)
mean_stack[amp].data = meanstack_image.getArray()
var_stack[amp].data = varstack_image.getArray()
sum_stack[amp].data = sumstack_image.getArray()
median_stack[amp].data = medianstack_image.getArray()
if bitpix is not None:
imutils.set_bitpix(output[amp], bitpix)
fitsWriteto(mean_stack,
'mean_image_' + exp + '.fits',
clobber=True)
fitsWriteto(var_stack, 'var_image_' + exp + '.fits', clobber=True)
fitsWriteto(sum_stack, 'sum_image_' + exp + '.fits', clobber=True)
fitsWriteto(median_stack,
'median_image_' + exp + '.fits',
clobber=True)
