edges_analysis.averaging.averaging.bin_data

edges_analysis.averaging.averaging.bin_data(data: ndarray, residuals: ndarray | None = None, weights: ndarray | None = None, bins: list[ndarray | slice] | None = None, axis: int = -1) → tuple[ndarray, ndarray, ndarray][source]

Bin data, in an un-biased way if possible.

This uses the estimator from memo #183: http://loco.lab.asu.edu/wp-content/uploads/2020/10/averaging_with_weights.pdf.

Parameters:

  • data – The data to be binned.

  • residuals – The residuals of the data, if known. If not provided, and weights is non uniform, the average will be biased.

  • weights – The weights of the data. If not provided, assume all weights are unity.

  • bins – The bins into which to bin the data. If not provided, assume a single bin encompassing all the data. Each element should be either an array that indexes into the axis over which to bin, or a slice object.

  • axis – The axis over which to bin.

Returns:

  • data – The binned data.

  • weights – The weights of the binned data.

  • residuals – The binned residuals (if provided).