def test_instantiate_from_one(shane_kast_blue_sci_files):
"""
Run on a single science frame
"""
#
det = 1
# Load calibrations
pixelflat = load_kast_blue_masters(pixflat=True)[0]
bpm = kast_blue.empty_bpm(shane_kast_blue_sci_files[0], det)
# Process steps
bias = None
par = kast_par['scienceframe']['process']
process_steps = procimg.init_process_steps(bias, par)
process_steps += ['trim', 'apply_gain', 'orient']
process_steps += ['flatten']
process_steps += ['extras']
process_steps += ['crmask']
# Load
rawImage = rawimage.RawImage(shane_kast_blue_sci_files[0], kast_blue, det)
processRawImage = processrawimage.ProcessRawImage(rawImage,
kast_par['scienceframe']['process'])
pypeItImage = processRawImage.process(process_steps, pixel_flat=pixelflat)
# Do it
sciImg = scienceimage.ScienceImage(kast_blue, det, kast_par['scienceframe']['process'],
pypeItImage.image, pypeItImage.ivar, bpm)
def build_stack(self, files, bpm, reject_cr=False):
"""
Generate a set of stack arrays useful for image processing
These are generated from the internal ProcessImage objects
held in self.pimages which need to have been previously
generated/loaded
Note: To save on memory, the image attributes of each
ProcessImage in self.pimages is reset after it contributes
to the stack arrays.
Args:
bpm (np.ndarray):
Bad pixel mask image
reject_cr (bool, optional):
If true, generate a CR image
Returns:
tuple: np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray
sciimg_stack, sciivar_stack, rn2img_stack, crmask_stack, mask_stack
"""
# Get it ready
nimages = len(files)
shape = (nimages, bpm.shape[0], bpm.shape[1])
sciimg_stack = np.zeros(shape)
sciivar_stack = np.zeros(shape)
rn2img_stack = np.zeros(shape)
crmask_stack = np.zeros(shape, dtype=bool)
# Mask
bitmask = maskimage.ImageBitMask()
mask_stack = np.zeros(shape, bitmask.minimum_dtype(asuint=True))
# Loop on the files
for kk, ifile in enumerate(files):
# Instantiate
sciImage = scienceimage.ScienceImage(self.spectrograph, self.det,
self.par['process'], bpm)
# Process
sciimg_stack[kk, :, :] = sciImage.process_raw(
ifile, self.bias, self.pixel_flat, illum_flat=self.illum_flat)
# Construct raw variance image and turn into inverse variance
sciivar_stack[kk, :, :] = sciImage.build_ivar()
# Mask cosmic rays
if reject_cr:
crmask_stack[kk, :, :] = sciImage.build_crmask()
# Build read noise squared image
rn2img_stack[kk, :, :] = sciImage.build_rn2img()
# Final mask for this image
mask_stack[kk, :, :] = sciImage.build_mask(
saturation=self.spectrograph.detector[self.det -
1]['saturation'],
mincounts=self.spectrograph.detector[self.det -
1]['mincounts'])
# Return
return sciimg_stack, sciivar_stack, rn2img_stack, crmask_stack, mask_stack
def test_standard_instantiate():
"""
Simple instantiate
"""
# Empty
bpm = np.zeros((100, 100))
sciImg = scienceimage.ScienceImage(kast_blue, [],
kast_par['scienceframe']['process'],
bpm)
assert sciImg.nfiles == 0
def test_standard_instantiate():
"""
Simple instantiate
"""
# Empty
bpm = np.zeros((100,100))
det = 1
sciImg = scienceimage.ScienceImage(kast_blue, det, kast_par['scienceframe']['process'],
np.zeros_like(bpm),
np.zeros_like(bpm),
bpm)