liger_iris_pipeline.combine_frames

class liger_iris_pipeline.combine_frames.CombineFramesStep(config_file: str | None = None, **kwargs)[source]

CombineFramesStep: Combines a set of 2D frames.

process(input: list[str | LigerIRISDataModel])[source]: This is where real work happens. Every Step subclass has to override this method. The default behaviour is to raise a NotImplementedError exception. The signature must be process(self, input : str | LigerIRISDataModel).

spec = "\n method = string(default = 'mean') # Method for combining the frames - 'mean', 'wmean', 'median', 'wmedian'.\n do_sigma_clip = boolean(default = True) # Whether to do sigma clipping. Sigma clipping is based on the biweight location and biweight midvariance (both unweighted), regardless of the 'method' parameter.\n sigma_thresh_low = float(default = 4) # Number of sigma for low outlier rejection.\n sigma_thresh_high = float(default = 4) # Number of sigma for high outlier rejection.\n thresh_low = float(default = None) # Low threshold for outlier rejection.\n thresh_high = float(default = None) # High threshold for outlier rejection.\n num_mask_low = integer(default = None) # Number of low outliers to mask.\n num_mask_high = integer(default = None) # Number of high outliers to mask.\n min_batch_size = integer(default = 3) # Minimum batch size for sigma clipping.\n maxiters = integer(default = 50) # Maximum number of iterations for sigma clipping.\n error_calc = string(default = 'measure') # Method for calculating the error - 'measure' or 'propagate'. Default is 'measure'.\n target_model = string(default = None) # Model type for the output. Default is the same as the input.\n "

Modules