ImCombiner#
- class astropop.image.imcombine.ImCombiner(max_memory=1000000000.0, dtype=<class 'numpy.float64'>, tmp_dir=None, use_disk_cache=False, **kwargs)#
Bases:
object
Process the combining operation of images, like the IRAF imcombine.
Methods Summary
combine
(image_list, method, **kwargs)Perform the image combining.
set_merge_header
(strategy[, keys])Set the strategy to merge headers during combination.
set_minmax_clip
([min_value, max_value])Enable minmax clipping during the combine.
set_sigma_clip
([sigma_limits, center_func, ...])Enable sigma clipping during the combine.
Methods Documentation
- combine(image_list, method, **kwargs)#
Perform the image combining.
- Parameters:
- image_list: `list` or `tuple`
List containing the images to be combined. The values in the list must be all of the same type and
FrameData
supported.- method: {‘mean’, ‘median’, ‘sum’}
Combining method.
- **kwargs:
- sum_normalize: bool (optional)
If True, the imaged will be multiplied, pixel by pixel, by the number of images divided by the number of non-masked pixels. This will avoid discrepancies by different numbers of masked pixels across the image. If False, the raw sum of images will be returned. Default: True
- Returns:
- combined:
FrameData
The combined image.
- combined:
Notes
For now, it don’t consider WCS, so it perform plain between the images, whitout registering.
Clipping parameters are set using class functions.
If the images exceed the maximum memory allowed, they are splited to perform the median and mean combine.
Masked elements are skiped. Result pixels will be masked if all the source pixels combined in it are also masked.
- set_merge_header(strategy, keys=None)#
Set the strategy to merge headers during combination.
- Parameters:
- strategy: {‘no_merge’, ‘first’, ‘only_equal’, ‘selected_keys’}
Header merging strategy.
- keys: list
List of the keys to be used for
selected_keys
strategy.
- set_minmax_clip(min_value=None, max_value=None)#
Enable minmax clipping during the combine.
- set_sigma_clip(sigma_limits=None, center_func='median', dev_func='mad_std')#
Enable sigma clipping during the combine.
- Parameters:
- sigma_limits: `float`, `tuple` or `None` (optional)
Set the low and high thresholds for sigma clipping. A number is applyed to both low and high limits. A tuple will be considered (low, high) limits.
None
disable the clipping. Default:None
- center_func: callable or {‘median’, ‘mean’} (optional)
Function to compute de central tendency of the data. Default: ‘median’
- dev_func: callable or {‘std’, ‘mad_std’} (optional)
Function to compute the deviation sigma for clipping. Defautl: ‘mad_std’
Notes
‘median’ and ‘mad_std’ gives a much better sigma clipping than ‘mean’ and ‘std’.