smx.zones.extraction#

Extract spectral zones from a DataFrame based on numeric column boundaries.

Functions#

extract_spectral_zones(→ Dict[str, pandas.DataFrame])

Extract spectral zones from a DataFrame based on specified cuts.

Module Contents#

smx.zones.extraction.extract_spectral_zones(Xcal: pandas.DataFrame, cuts: List[Tuple | dict]) → Dict[str, pandas.DataFrame][source]#

Extract spectral zones from a DataFrame based on specified cuts.

Parameters#

Xcalpd.DataFrame

DataFrame with spectral data. Columns must be numeric (or convertible to numeric) values representing wavelengths / energies.

cutslist of tuples/lists or dicts

Each item defines a spectral zone to extract.

  • (start, end) — zone boundaries; name defaults to "start-end"

  • (name, start, end) — named zone

  • (name, start, end, group) — named zone assigned to a group

  • {'name': str, 'start': float, 'end': float} — dict form

  • {'name': str, 'start': float, 'end': float, 'group': str} — dict form with grouping

When multiple cuts share the same group value their column subsets are concatenated into a single zone keyed by the group name. Cuts without a group are extracted individually under their own name, as before.

Returns#

dict[str, pd.DataFrame]

Dictionary where keys are zone names (or group names) and values are DataFrames with the extracted spectral data (same row index as Xcal).

Examples#

>>> zones = extract_spectral_zones(X, [('Ca ka', 3.6, 3.7), ('Fe ka', 6.3, 6.5)])
>>> zones['Ca ka'].shape
(n_samples, n_cols_in_Ca_ka_zone)

Group background regions into a single zone:

>>> cuts = [
...     ('background 1', 1.0, 101.0, 'background'),
...     ('Feature 1',  101.0, 193.3),
...     ('background 2', 193.3, 255.4, 'background'),
...     ('Feature 2',  255.4, 341.6),
... ]
>>> zones = extract_spectral_zones(X, cuts)
>>> 'background' in zones   # True — merged from background 1 & 2
True
>>> 'background 1' in zones  # False — individual cuts absorbed into group
False