https://github.com/qbarthelemy/pyspectrum
Python package for spectral analyses, looking at sampling rate conversion, spectral rescaling and peak detection.
https://github.com/qbarthelemy/pyspectrum
aliasing python sample-rate-conversion scientific-computing signal-processing spectral-analysis spectral-normalization
Last synced: 4 months ago
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Python package for spectral analyses, looking at sampling rate conversion, spectral rescaling and peak detection.
- Host: GitHub
- URL: https://github.com/qbarthelemy/pyspectrum
- Owner: qbarthelemy
- Created: 2022-08-18T19:15:07.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-04-01T09:16:27.000Z (over 2 years ago)
- Last Synced: 2024-10-07T19:49:53.046Z (12 months ago)
- Topics: aliasing, python, sample-rate-conversion, scientific-computing, signal-processing, spectral-analysis, spectral-normalization
- Language: Python
- Homepage:
- Size: 401 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PySpectrum
[](https://img.shields.io/badge/python-3.8+-blue)
[](https://img.shields.io/badge/license-BSD--3--Clause-green)
PySpectrum is a Python package for spectral analyses:
- sampling rate conversion and its potential aliasing artifacts;
- spectral rescaling and spectral peak detection by robust log-log linear regression.
PySpectrum is distributed under the open source 3-clause BSD license.
## Description
### Sample-rate conversion (SRC)
In signal processing,
[sample-rate conversion](https://en.wikipedia.org/wiki/Sample-rate_conversion)
(SRC) (also called resampling) is the process of changing the sampling rate of a discrete signal:
[downsampling](https://en.wikipedia.org/wiki/Downsampling_(signal_processing))
or [upsampling](https://en.wikipedia.org/wiki/Upsampling).
SRC can generate
[aliasing artifacts](https://en.wikipedia.org/wiki/Aliasing) when
[Nyquist–Shannon sampling theorem](https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem)
is not respected.
This module implements several functions to complete the
[Signal processing module of SciPy](https://docs.scipy.org/doc/scipy/reference/signal.html#filtering):
- `downsample` and `upsample` for multidimensional arrays,
- `upiirdn` the [scipy.signal.upfirdn](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html)
counterpart for IIR filtering, allowing **fractional** downsampling in sample domain contrary to
[scipy.signal.decimate](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.decimate.html).
#### 2D image
Image downsampled without anti-aliasing filter shows
[aliasing artifacts](https://en.wikipedia.org/wiki/Aliasing).
This kind of downsampling is present in max-pooling, strided-convolution
and more generally in strided-layers.
These architectural components are widely used in convolutional neural networks
like [ResNets](https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf),
[DenseNets](https://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf),
[MobileNets](https://openaccess.thecvf.com/content_cvpr_2018/papers/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.pdf),
degrading performances in terms of
[shift-invariance and classification accuracy](http://proceedings.mlr.press/v97/zhang19a/zhang19a.pdf).
See `examples\downsample_img.py` for the complete analysis.
#### Time series
Time series of this example is an electrocardiogram (ECG) signal,
downsampled from 360 Hz to 180 Hz.
Signal downsampled without anti-aliasing filter shows aliasing artifacts,
ie. a huge artifact peak at 80 Hz.
This kind of downsampling can be present in quickly coded drivers.
See `examples\downsample_ecg.py` for the complete analysis.
### Spectral processing
This module implements several functions:
- `rescale` to rescale spectra using a robust log-log linear regression,
- `detect_peak` to detect peak on spectra following a power law.
#### Spectral rescaling
Many analyses require a normalization / standardization step of spectra,
to remove inter-spectra variabilities.
See `examples\rescale_simulated.py` for the complete analysis.
#### Alpha peak detection in EEG spectrum
Automatic detection of alpha peak in the spectrum of an electroencephalogram
(EEG), compared to peak finders provided by
[SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html)
and [MNE-Python](https://mne.tools/stable/generated/mne.preprocessing.peak_finder.html).
See `examples\find_peak_eeg.py` for the complete analysis.
## Installation
#### From sources
To install PySpectrum as a standard module:
```shell
pip install path/to/PySpectrum
```
To install PySpectrum in editable / development mode, in the folder:
```shell
pip install poetry
poetry install
```
## Testing
Use `pytest`.