detect_outliers

jwst.outlier_detection.spec.detect_outliers(input_models, save_intermediate_results, good_bits, maskpt, snr1, snr2, scale1, scale2, backg, resample_data, weight_type, pixfrac, kernel, fillval, make_output_path)[source]

Flag outliers in slit-like spectroscopic data.

Parameters:

  • input_models (ModelContainer) – A container of data models.

  • save_intermediate_results (bool) – If True, save intermediate results.

  • good_bits (int) – Bit values indicating good pixels.

  • maskpt (float) – The percentage of the mean weight to use as a threshold for masking.

  • snr1 (float) – The signal-to-noise ratio threshold for first pass flagging, prior to smoothing.

  • snr2 (float) – The signal-to-noise ratio threshold for secondary flagging, after smoothing.

  • scale1 (float) – Scale factor used to scale the absolute derivative of the blot model for the first pass.

  • scale2 (float) – Scale factor used to scale the absolute dervative of the blot model for the second pass.

  • backg (float) – Scalar background level to add to the blotted image. Ignored if input_model.meta.background.level is not None but input_model.meta.background.subtracted is False.

  • resample_data (bool) – If True, resample the data before detecting outliers.

  • weight_type (str) – The type of weighting kernel to use when resampling. Options are ‘ivm’ or ‘exptime’.

  • pixfrac (float) – The pixel shrinkage factor to pass to drizzle.

  • kernel (str) – The flux distribution kernel function to use when resampling.

  • fillval (str) – The value to use in the output for pixels with no weight or flux

  • make_output_path (function) – The functools.partial instance to pass to save_blot. Must be specified if save_blot is True.

Returns:

The input models with outliers flagged.

Return type:

ModelContainer