def _combine_calib_images(
images: List[Image], bias: Optional[Image] = None, normalize: bool = False, method: str = "average"
) -> Image:
"""Combine a list of given images.
Args:
images: List of images to combine.
bias: If given, subtract from images before combining them.
normalize: If True, images are normalized to median of 1 before and after combining them.
method: Method for combining images.
"""
import ccdproc
# get CCDData objects
data = [image.to_ccddata() for image in images]
# subtract bias?
if bias is not None:
bias_data = bias.to_ccddata()
data = [ccdproc.subtract_bias(d, bias_data) for d in data]
# normalize?
if normalize:
data = [d.divide(np.median(d.data), handle_meta="first_found") for d in data]
# combine image
combined = ccdproc.combine(
data,
method=method,
sigma_clip=True,
sigma_clip_low_thresh=5,
sigma_clip_high_thresh=5,
mem_limit=350e6,
unit="adu",
combine_uncertainty_function=np.ma.std,
)
# normalize?
if normalize:
combined = combined.divide(np.median(combined.data), handle_meta="first_found")
# to Image and copy header
image = Image.from_ccddata(combined)
# add history
for i, src in enumerate(images, 1):
basename = src.header["FNAME"].replace(".fits.fz", "").replace(".fits", "")
image.header["L1AVG%03d" % i] = (basename, "Image used for average")
image.header["RLEVEL"] = (1, "Reduction level")
# finished
return image
async def __call__(self, image: Image) -> Image:
"""Calibrate an image.
Args:
image: Image to calibrate.
Returns:
Calibrated image.
"""
import ccdproc
# get calibration masters
try:
bias = await self._find_master(image, ImageType.BIAS)
dark = await self._find_master(image, ImageType.DARK)
flat = await self._find_master(image, ImageType.SKYFLAT)
except ValueError as e:
log.error("Could not find calibration frames: " + str(e))
return image
# calibrate image
c = await asyncio.get_running_loop().run_in_executor(
None,
partial(
ccdproc.ccd_process,
image.to_ccddata(),
oscan=image.header["BIASSEC"]
if "BIASSEC" in image.header else None,
trim=image.header["TRIMSEC"]
if "TRIMSEC" in image.header else None,
error=True,
master_bias=bias.to_ccddata() if bias is not None else None,
dark_frame=dark.to_ccddata() if dark is not None else None,
master_flat=flat.to_ccddata() if flat is not None else None,
bad_pixel_mask=None,
gain=image.header["DET-GAIN"] * u.electron / u.adu,
readnoise=image.header["DET-RON"] * u.electron,
dark_exposure=dark.header["EXPTIME"] *
u.second if dark is not None else None,
data_exposure=image.header["EXPTIME"] * u.second,
dark_scale=True,
gain_corrected=False,
),
)
# to image
calibrated = Image.from_ccddata(c)
calibrated.header["BUNIT"] = ("electron", "Unit of pixel values")
# set raw filename
if "ORIGNAME" in image.header:
calibrated.header["L1RAW"] = image.header["ORIGNAME"].replace(
".fits", "")
# add calibration frames
if bias is not None:
calibrated.header["L1BIAS"] = (
bias.header["FNAME"].replace(".fits.fz",
"").replace(".fits", ""),
"Name of BIAS frame",
)
if dark is not None:
calibrated.header["L1DARK"] = (
dark.header["FNAME"].replace(".fits.fz",
"").replace(".fits", ""),
"Name of DARK frame",
)
if flat is not None:
calibrated.header["L1FLAT"] = (
flat.header["FNAME"].replace(".fits.fz",
"").replace(".fits", ""),
"Name of FLAT frame",
)
# set RLEVEL
calibrated.header["RLEVEL"] = (1, "Reduction level")
# finished
return calibrated