tuskitoo.utils.utils module

sigma_clip_replace(data, sigma=3.0, max_iter=5, replace_with='mean', **kwargs)

Perform iterative sigma-clipping on ‘data’ and replace outliers with either the mean or median of the valid data in each iteration.

Parameters:

• data (array_like) – 1D or 2D numpy array of your data (e.g. pixel values).

• sigma (float, optional) – Sigma-clipping limit (number of standard deviations).

• max_iter (int, optional) – Maximum number of iterations.

• replace_with ({'mean', 'median'}, optional) – Replacement strategy for outliers.

Returns:

• clipped_data (numpy.ndarray) – Copy of ‘data’ with outliers replaced.

• mask (numpy.ndarray (bool)) – Boolean mask array of the same shape as data. True indicates pixels considered outliers in the final iteration.

clipping_region(image, pieces, slice_, sigma=2.0, max_iter=5, replace_with='mean', where_is_the_signal=[])

sigma_clip_1d(spectra, region_size=10, sigma=2, max_iter=5, replace_with='median', **kwargs)