Outlier Detection Utils

stcal.outlier_detection.median Module

Compute median of large datasets in memory- and runtime-efficient ways.

nanmedian3D(cube[, overwrite_input])

Compute the nanmedian of a cube.

MedianComputer(full_shape, in_memory[, ...])

Top-level class to treat median computation uniformly, whether in memory or on disk.

Utility functions for outlier detection routines

`medfilt`(arr, kern_size)	scipy.signal.medfilt (and many other median filters) have undefined behavior for nan inputs.
`compute_weight_threshold`(weight, maskpt)	Compute the weight threshold for a single image or cube.
`flag_crs`(sci_data, sci_err, blot_data, snr)	Straightforward detection of outliers for non-dithered data since sci_err includes all noise sources (photon, read, and flat for baseline).
`flag_resampled_crs`(sci_data, sci_err, ...)	Detect outliers (CRs) using resampled reference data.
`gwcs_blot`(median_data, median_wcs, ...[, ...])	Resample the median data to recreate an input image based on the blot wcs.
`calc_gwcs_pixmap`(in_wcs, out_wcs, in_shape)	Return a pixel grid map from input frame to output frame.
`reproject`(wcs1, wcs2)	Given two WCSs return a function which takes pixel coordinates in wcs1 and computes them in wcs2.