mioXpektron.denoise.denoise_batch

Batch-oriented helpers for running denoising over many spectra files.

Functions

`batch_denoise`(files, output_dir[, method, ...])	Apply the configured denoising method to multiple spectrum files.
`load_txt_spectrum`(path)	Parse a plain-text spectrum file into NumPy arrays using Polars.
`save_txt_spectrum`(orig, out_path, arrays)	Persist a spectrum to disk with the same column ordering as the input using Polars.

Classes

BatchResult(file, out_file, status, ...[, ...])

Outcome metadata for a single denoising file run.

mioXpektron.denoise.denoise_batch.load_txt_spectrum(path)[source]

Parse a plain-text spectrum file into NumPy arrays using Polars.

Parameters:: path (Path) – Location of the ASCII export produced by the instrument or pre-processing software. Delimiters can be comma, tab, semicolon, or space.
Returns:: Dictionary containing any detected columns (channel, mz, and intensity). Only columns that successfully parse are included; the intensity array is always provided.
Return type:: dict

Notes

The loader uses Polars for fast CSV parsing with automatic delimiter detection, tolerates blank lines, and skips rows that fail numeric conversion.

mioXpektron.denoise.denoise_batch.save_txt_spectrum(orig, out_path, arrays)[source]

Persist a spectrum to disk with the same column ordering as the input using Polars.

Parameters:

orig (Path) – Original source file. Currently unused but kept for potential metadata handling.
out_path (Path) – Destination path for the denoised export.
arrays (dict[str, np.ndarray]) – Columns to write. The function writes whichever of channel, mz, and intensity are present, preserving numeric precision to six decimals.

Return type:

None

class mioXpektron.denoise.denoise_batch.BatchResult(file, out_file, status, elapsed_s, n_points, message='')[source]

Bases: object

Outcome metadata for a single denoising file run.

Parameters:

file (str)
out_file (str | None)
status (str)
elapsed_s (float)
n_points (int)
message (str)

file: str

out_file: str | None

status: str

elapsed_s: float

n_points: int

message: str = ''

mioXpektron.denoise.denoise_batch.batch_denoise(files, output_dir, method='wavelet', n_workers=0, backend='threads', progress=True, params=None)[source]

Apply the configured denoising method to multiple spectrum files.

Parameters:

files (Iterable[str | Path]) – Collection of filesystem paths (glob results, manual list, etc.).
output_dir (str | Path) – Directory where the denoised outputs will be written.
method (str, default "wavelet") – Name of the smoothing routine forwarded to noise_filtering().
n_workers (int, default 0) – Worker count for the executor. 0 or None selects a CPU-aware default.
backend ({"threads", "processes"}, default "threads") – Execution strategy for the worker pool.
progress (bool, default True) – If True, wrap the executor iterator in tqdm when available.
params (dict | None) – Extra keyword arguments forwarded to noise_filtering().

Returns:

Status records describing each attempted file.

Return type:

list[BatchResult]

Raises:

ValueError – If no input paths exist or an unsupported backend name is provided.