mioXpektron.normalization.main

High-level orchestration helpers for normalising ToF-SIMS spectra.

Classes

NormalizationMethods(mz_values, raw_intensities)

Evaluate and apply normalization strategies for ToF-SIMS data.

class mioXpektron.normalization.main.NormalizationMethods(mz_values, raw_intensities)

Bases: object

Evaluate and apply normalization strategies for ToF-SIMS data.

Parameters:

• mz_values (array-like) – The m/z axis shared by all spectra.

• raw_intensities (array-like) – Raw intensity values aligned with mz_values.

apply(method='tic', **kwargs)

Apply a named normalization to the stored spectrum.

Parameters:

• method (str) – Normalization method name (see normalization_method_names()).

• **kwargs – Method-specific keyword arguments.

Returns:

Normalized intensity array.

Return type:

np.ndarray

compare_visual(methods=None, method_kwargs_map=None, mz_min=0, mz_max=500, sample_name='test', group=None, figsize=(12, 8), save_plot=True)

Plot the raw spectrum alongside several normalized versions.

Parameters:

• methods (list of str, optional) – Normalization methods to overlay. Defaults to a curated set.

• method_kwargs_map (dict, optional) – {method: {kwarg: value}} for method-specific parameters.

• mz_min (float) – m/z bounds for the preview window.

• mz_max (float) – m/z bounds for the preview window.

• sample_name (str) – Label used for file naming.

• group (str or None) – Group identifier.

• figsize (tuple) – Figure size.

• save_plot (bool) – Persist the rendered figure.

Return type:

matplotlib.axes.Axes

normalize_and_check(method='tic', method_kwargs=None, *, sample_name='test', group=None, mz_min=0, mz_max=500, show_peaks=False, peak_height=1000, peak_prominence=50, min_peak_width=1, max_peak_width=None, figsize=(10, 6), save_plot=True)

Apply one normalization and visualise the result with peak overlay.

Parameters:

• method (str) – Normalization method.

• method_kwargs (dict, optional) – Extra kwargs forwarded to normalize().

• sample_name (str) – Plot labels.

• group (str) – Plot labels.

• mz_min (float) – m/z window for the plot.

• mz_max (float) – m/z window for the plot.

• show_peaks (bool) – Annotate detected peaks.

• peak_height (float) – Peak detection tuning passed to PlotPeak.

• peak_prominence (float) – Peak detection tuning passed to PlotPeak.

• min_peak_width (int) – Peak detection tuning passed to PlotPeak.

• max_peak_width (int | None) – Peak detection tuning passed to PlotPeak.

• figsize (tuple)

• save_plot (bool)

Return type:

matplotlib.axes.Axes

static evaluate(files, methods=None, method_kwargs_map=None, mz_min=None, mz_max=None, n_jobs=-1, compute_supervised=True, save_results=True)

Evaluate normalization methods across multiple spectra files.

Thin wrapper around NormalizationEvaluator that runs evaluation, prints a summary, and optionally saves results.

Parameters:

• files (list of str or Path) – Spectrum file paths or glob patterns.

• methods (list of str, optional) – Method names to evaluate.

• method_kwargs_map (dict, optional) – Per-method keyword arguments.

• mz_min (float, optional) – m/z range for data import.

• mz_max (float, optional) – m/z range for data import.

• n_jobs (int) – Parallel workers (-1 = all CPUs).

• compute_supervised (bool) – Run supervised classification (requires scikit-learn).

• save_results (bool) – Save CSV + JSON + plots to OUTPUT_DIR.

Returns:

The evaluator instance (call .plot() for figures).

Return type:

NormalizationEvaluator

static available_methods()

Return sorted list of available normalization method names.

Return type:

List[str]