def test_bias_image(self):
ccd = MaskedCCD(self.image_file)
overscan = makeAmplifierGeometry(self.image_file)
ccd_mean = MaskedCCD(self.mean_image_file)
overscan_mean = makeAmplifierGeometry(self.mean_image_file)
for amp in ccd:
for method in ['mean', 'row', 'func', 'spline']:
if method == 'mean':
my_bias_image = imutils.bias_image(
ccd_mean[amp],
overscan_mean.serial_overscan,
bias_method=method,
**self.kwargs)
fracdiff = ((self.mean_bias_image.getArray() -
my_bias_image.getArray()) /
self.mean_bias_image.getArray())
self.assertTrue(max(np.abs(fracdiff.flat)) < 1.5e-3)
else:
my_bias_image = imutils.bias_image(
ccd[amp],
overscan.serial_overscan,
bias_method=method,
**self.kwargs)
fracdiff = ((self.bias_image.getArray() -
my_bias_image.getArray()) /
self.bias_image.getArray())
self.assertTrue(max(np.abs(fracdiff.flat)) < 1e-6)
my_bias_image = imutils.bias_image(ccd[amp],
self.amp_geom.serial_overscan)
fracdiff = (
(self.bias_image.getArray() - my_bias_image.getArray()) /
self.bias_image.getArray())
self.assertTrue(max(np.abs(fracdiff.flat)) < 1e-6)
def bias_subtracted_image(image, bias_image, overscan, bias_method='row'):
# Make deep copies of image and bias image so that we can modify them.
im_out = image.Factory(image)
bias_sub = bias_image.Factory(bias_image)
# Subtract overscans.
bias_sub -= imutils.bias_image(im=bias_image,
overscan=overscan,
bias_method=bias_method)
im_out -= imutils.bias_image(im=image,
overscan=overscan,
bias_method=bias_method)
# Subtract remaining strucutured bias.
im_out -= bias_sub
return im_out
def get_ccd_data(self, ccd, data, **kwargs):
"""Get the bias values and update the data dictionary
Parameters
----------
ccd : `MaskedCCD`
The ccd we are getting data from
data : `dict`
The data we are updating
Keywords
--------
slot : `str`
The slot name
ifile : `int`
The file index
nfiles_used : `int`
Total number of files
"""
slot = self.config.slot
bias_type = self.get_bias_algo()
ifile = kwargs['ifile']
nfiles = kwargs['nfiles']
amps = get_amp_list(ccd)
for i, amp in enumerate(amps):
regions = get_geom_regions(ccd, amp)
serial_oscan = regions['serial_overscan']
img = get_raw_image(ccd, amp)
bimg = imutil.bias_image(img, serial_oscan, bias_method=bias_type)
bimg_row_mean = bimg[serial_oscan].getArray().mean(1)
key_str = "biasval_%s_a%02i" % (slot, i)
if key_str not in data:
data[key_str] = np.ndarray((len(bimg_row_mean), nfiles))
data[key_str][:, ifile] = bimg_row_mean
def superflat(files,
bias_frame=None,
outfile='superflat.fits',
bitpix=None,
bias_subtract=True,
bias_method='row'):
"""
The superflat is created by bias-offset correcting the input files
and median-ing them together.
"""
# Get overscan region.
overscan = makeAmplifierGeometry(files[0]).serial_overscan
output_images = dict()
for amp in imutils.allAmps(files[0]):
images = []
for infile in files:
image = afwImage.ImageF(infile, imutils.dm_hdu(amp))
if bias_subtract:
if bias_frame:
bias_image = afwImage.ImageF(bias_frame,
imutils.dm_hdu(amp))
image = bias_subtracted_image(image, bias_image, overscan,
bias_method)
else:
image -= imutils.bias_image(im=image,
overscan=overscan,
bias_method=bias_method)
images.append(image)
if lsst.afw.__version__.startswith('12.0'):
images = afwImage.vectorImageF(images)
output_images[amp] = afwMath.statisticsStack(images, afwMath.MEDIAN)
imutils.writeFits(output_images, outfile, files[0])
return outfile
def bias_image_using_overscan(self, amp, overscan=None, **kwargs):
"""
Generate a bias image containing offset values calculated from
bias(), bias_row(), bias_func() or bias_spline(). The default bias method
is set to bias_row() in image_utils.py. Keyword arguments can be passed
depending on which bias method is used.
Keyword Arguments:
fit_order: The order of the polynomial. This only needs to be specified when
using the 'func' method. The default is: 1.
k: The degree of the spline fit. This only needs to be specified when using
the 'spline' method. The default is: 3.
s: The amount of smoothing to be applied to the fit. This only needs to be
specified when using the 'spline' method. The default is: 18000.
t: The number of knots. If None, finds the number of knots to use for a given
smoothing factor, s. This only needs to be specified when using the 'spline'
method. The default is: None.
"""
if overscan is None:
overscan = self.amp_geom.serial_overscan
try:
return imutils.bias_image(self._deep_copy(amp),
overscan=overscan,
**kwargs)
except pexExcept.LSST_RUNTIME_EXCEPTION as eobj:
raise MaskedCCDBiasImageException(
"DM stack error generating bias " +
"image from overscan region:\n" + str(eobj))
def superflat(files,
bias_frame=None,
outfile='superflat.fits',
bitpix=-32,
bias_subtract=True):
"""
The superflat is created by bias-offset correcting the input files
and median-ing them together.
"""
# Get overscan region.
overscan = makeAmplifierGeometry(files[0]).serial_overscan
# Use the first file as a template for the fits output.
output = fits.open(files[0])
for amp in imutils.allAmps(files[0]):
images = afwImage.vectorImageF()
for infile in files:
image = afwImage.ImageF(infile, imutils.dm_hdu(amp))
if bias_subtract:
if bias_frame:
bias_image = afwImage.ImageF(bias_frame,
imutils.dm_hdu(amp))
image = bias_subtracted_image(image, bias_image, overscan)
else:
image -= imutils.bias_image(image,
overscan,
statistic=np.median)
images.push_back(image)
median_image = afwMath.statisticsStack(images, afwMath.MEDIAN)
output[amp].data = median_image.getArray()
if bitpix is not None:
imutils.set_bitpix(output[amp], bitpix)
fitsWriteto(output, outfile, clobber=True)
return outfile
def test_bias_image(self):
ccd = MaskedCCD(self.image_file)
for amp in ccd:
my_bias_image = imutils.bias_image(ccd[amp],
self.amp_geom.serial_overscan)
fracdiff = (
(self.bias_image.getArray() - my_bias_image.getArray()) /
self.bias_image.getArray())
self.assertTrue(max(np.abs(fracdiff.flat)) < 1e-6)
def bias_subtracted_image(image,
bias_image,
overscan,
fit_order=1,
statistic=np.median):
# Make deep copies of image and bias image so that we can modify them.
im_out = image.Factory(image)
bias_sub = bias_image.Factory(bias_image)
# Subtract overscans.
bias_sub -= imutils.bias_image(bias_image,
overscan,
fit_order=fit_order,
statistic=statistic)
im_out -= imutils.bias_image(image,
overscan,
fit_order=fit_order,
statistic=statistic)
# Subtract remaining strucutured bias.
im_out -= bias_sub
return im_out
def bias_image_using_overscan(self, amp, overscan=None, fit_order=1):
if overscan is None:
overscan = self.amp_geom.serial_overscan
try:
return imutils.bias_image(self[amp],
overscan=overscan,
fit_order=fit_order)
except pexExcept.LSST_RUNTIME_EXCEPTION as eobj:
raise MaskedCCDBiasImageException(
"DM stack error generating bias " +
"image from overscan region:\n" + str(eobj))