scitex-dsp 0.1.9

Digital signal processing (PAC, Hilbert, Wavelet, filters, demo signals, …) — standalone module from the SciTeX ecosystem

scitex-dsp

Digital signal processing for scientific Python — PAC, Hilbert, wavelet, filters, resampling, demo signals.

Full Documentation · uv pip install scitex-dsp[all]

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Problem and Solution

#	Problem	Solution
1	Signal-processing pipelines mix NumPy, SciPy, MNE, and PyTorch with incompatible array shapes.	scitex_dsp exposes a uniform (batch, channel, time) 3-D contract via ensure_3d and the @torch_fn decorator.
2	Phase-Amplitude Coupling (PAC), wavelets, and ripple detection are scattered across one-off scripts.	First-class pac, wavelet, hilbert, detect_ripples, modulation_index reproducible primitives.
3	Demo signals for testing pipelines have to be re-rolled by every project.	demo_sig(sig_type=...) produces deterministic chirp / periodic / ripple test signals.

Installation

pip install scitex-dsp

Architecture

scitex_dsp is organised as flat building blocks plus four sub-modules. Public entry points compose left-to-right into typical neuroscience pipelines:

flowchart LR
    raw[(raw LFP / EEG)] --> ensure_3d
    ensure_3d --> reference[reference.common_average]
    reference  --> filt[filt.bandpass]
    filt --> hilbert
    hilbert --> psd
    hilbert --> wavelet
    wavelet --> modulation_index
    modulation_index --> pac
    hilbert --> detect_ripples[detect_ripples]
    psd  --> band_powers[per-band power]

src/scitex_dsp/
├── _hilbert.py   _psd.py        _wavelet.py
├── _pac.py       _modulation_index.py
├── _detect_ripples.py
├── _resample.py  _crop.py       _ensure_3d.py
├── _demo_sig.py  _transform.py
├── filt.py       norm.py        reference.py
├── add_noise.py  params.py      example.py
└── utils/        # ensure_*, zero-pad, differential filters

All public functions accept (channels, samples) or (batch, channels, samples).

2 Interfaces

Python API

import scitex_dsp as dsp

xx, tt, fs = dsp.demo_sig(sig_type="chirp", fs=1024)
psd, ff = dsp.psd(xx, fs)
xf = dsp.filt.bandpass(xx, fs, bands=[[8, 12]])
hp = dsp.hilbert(xx)
pac, freqs_pha, freqs_amp = dsp.pac(xx, fs)

Importable from the umbrella

import scitex
scitex.dsp.demo_sig(sig_type="chirp")  # `scitex.dsp` aliases `scitex_dsp`

Demo

A 13-notebook progressive tutorial lives in examples/, committed with executed cell outputs — read on GitHub without running anything locally.

flowchart LR
    A[01 demo_sig] --> B[02 ensure_3d / crop]
    B --> C[03 norm]
    C --> D[04 filt]
    D --> E[05 hilbert]
    E --> F[06 psd]
    E --> G[07 wavelet]
    D --> H[08 resample]
    A --> I[09 add_noise]
    B --> J[10 reference]
    E --> K[11 modulation_index]
    K --> L[12 pac]
    E --> M[13 detect_ripples]

See examples/README.md for the full index and suggested reading paths.

#	Notebook	Topic	Cross-check
01	01_demo_sig.ipynb	synthetic test signals — uniform / gauss / periodic / chirp	—
04	04_filt.ipynb	Butterworth bandpass / bandstop	—
05	05_hilbert.ipynb	analytic signal — phase + envelope	scipy.signal.hilbert
06	06_psd.ipynb	PSD + per-band integrated power	—
07	07_wavelet.ipynb	continuous wavelet transform	—
09	09_add_noise.ipynb	gauss / white / pink / brown — traces + PSDs	—
12	12_pac.ipynb	phase-amplitude coupling heatmap	tensorpac.Pac
13	13_detect_ripples.ipynb	end-to-end ripple detection — DataFrame + shaded events	—

Re-run them all with ./examples/00_run_all.sh.

Part of SciTeX

scitex-dsp is part of SciTeX.

Install via the umbrella with pip install scitex[dsp], then access as scitex.dsp or run scitex dsp from the CLI.

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

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