mioXpektron.normalization.preprocessing

Functions

batch_tic_norm(input_pattern[, output_dir, ...])	Batch‑import and preprocess multiple ToF‑SIMS spectra, then save the (m/z, normalized_intensity) arrays for each file as a tab‑separated text file in output_dir.
data_preprocessing(file_path[, mz_min, ...])	Import and preprocess ToF-SIMS data from a text file.
resample_spectrum(mz_values, ...[, method])	Resample a spectrum onto a target m/z grid.

Classes

BatchTicNorm(input_pattern[, output_dir, ...])

Batch TIC normalization for multiple spectra files using Polars and concurrent.futures.

mioXpektron.normalization.preprocessing.resample_spectrum(mz_values, intensity_values, target_mz, method='linear')

Resample a spectrum onto a target m/z grid.

The input axis is sorted, duplicate m/z positions are collapsed to their first occurrence, and values outside the native m/z range are filled with zero. Supported interpolation methods are linear, pchip, akima, makima, and cubic.

mioXpektron.normalization.preprocessing.data_preprocessing(file_path, mz_min=None, mz_max=None, normalization_target=1000000.0, verbose=True, return_all=False)

Import and preprocess ToF-SIMS data from a text file.

Parameters:

file_pathstr

Path to the ToF-SIMS data file

mz_min, mz_maxfloat, optional

m/z range to import

normalization_targetfloat or None

Target TIC for normalization, or None to skip

verbosebool

Print progress if True

return_allbool

If True, return all intermediate arrays

Returns:

mz_values : numpy.ndarray normalized_intensities : numpy.ndarray sample_name : str group : str (optionally: intermediate arrays)

mioXpektron.normalization.preprocessing.batch_tic_norm(input_pattern, output_dir='normalized_spectra', mz_min=None, mz_max=None, normalization_target=1000000.0, verbose=False)

Batch‑import and preprocess multiple ToF‑SIMS spectra, then save the (m/z, normalized_intensity) arrays for each file as a tab‑separated text file in output_dir.

Parameters:

• input_pattern (str) – Glob pattern (e.g. ‘spectra/*.txt’) that expands to the input files.

• output_dir (str) – Folder where ‘<original‑name>_normalized.txt’ will be written; created if it does not already exist.

• mz_min (float | None) – Passed through to :pyfunc:`data_preprocessing`.

• mz_max (float | None) – Passed through to :pyfunc:`data_preprocessing`.

• normalization_target (float | None) – Passed through to :pyfunc:`data_preprocessing`.

• verbose (bool) – Passed through to :pyfunc:`data_preprocessing`.

Returns:

Paths of the files written, in processing order.

Return type:

List[str]

class mioXpektron.normalization.preprocessing.BatchTicNorm(input_pattern, output_dir='normalized_spectra', normalization_target=1000000.0, n_workers=-1, verbose=True)

Bases: object

Batch TIC normalization for multiple spectra files using Polars and concurrent.futures.

Supports both CSV and TXT file formats: - CSV: Uses ‘corrected_intensity’ if available, otherwise ‘intensity’ - TXT: Tab-separated m/z and intensity values

Output files contain: channel, mz, intensity (normalized)

Parameters:

• input_pattern (str)

• output_dir (str)

• normalization_target (float)

• n_workers (int)

• verbose (bool)

__init__(input_pattern, output_dir='normalized_spectra', normalization_target=1000000.0, n_workers=-1, verbose=True)

Initialize BatchTicNorm processor.

Parameters:

• input_pattern (str) – Glob pattern for input files (e.g., ‘data/.csv’ or ‘data/.txt’)

• output_dir (str) – Directory to save normalized files

• normalization_target (float) – Target TIC value for normalization (default: 1e6)

• n_workers (int) – Number of parallel workers (default: 16)

• verbose (bool) – Print progress information

process()

Process all files using concurrent.futures.

Returns:

List of output file paths that were successfully created

Return type:

List[str]

get_tic_statistics()

Calculate TIC statistics for all input files before normalization.

Returns:

DataFrame with columns: filename, tic_original, tic_million

Return type:

pl.DataFrame