pyfli.data_cc.norm

Normalize FLI arrays with zero-one, min-max, reference-scale, peak, and PDF transforms.

This module belongs to pyfli.data_cc and is part of PyFLI array preprocessing helpers for normalization, masking, ROI extraction, and IRF alignment. Public API includes classes Normalization.

Classes

Normalization(data)

Run the normalization routine.

class Normalization(data)

Bases: object

Run the normalization routine. and exposes zero-one scaling, min-max scaling, reference scaling, global peak normalization, and probability-density conversion.

Parameters:

data (np.ndarray) – Array of values to normalize, mask, or summarize.

zerone(threshold=0)

Run the zerone routine.

Parameters:

threshold (int) – Threshold used to mask, classify, or validate data.

Returns:

Object produced by zerone.

Return type:

Any

minmax(threshold=0)

Run the minmax routine.

Parameters:

threshold (int) – Threshold used to mask, classify, or validate data.

Returns:

Object produced by minmax.

Return type:

Any

norm_scale(ref_data, threshold=0)

Run the norm scale routine.

Parameters:

• ref_data (np.ndarray) – Reference data used for normalization.

• threshold (int) – Threshold used to mask, classify, or validate data.

Returns:

Object produced by norm scale.

Return type:

Any

global_peak_norm_3d(threshold=0)

Run the global peak norm 3d routine.

Parameters:

threshold (int) – Threshold used to mask, classify, or validate data.

Returns:

Object produced by global peak norm 3d.

Return type:

Any

to_pdf(threshold=0)

Run the to PDF routine.

Parameters:

threshold (int) – Threshold used to mask, classify, or validate data.

Returns:

Object produced by to PDF.

Return type:

Any