spa-ds (originally qsi-tk)
Data science toolkit for spectroscopic profiling signals/data.
Installation
pip install spa-ds
Contents
This package is a master library containing various previous packages published by our team.
| module | sub-module | description | standalone pypi package | publication |
| spa.io | spa.io.load | File I/O, Dataset loading | Provides 40+ open datasets. 15+ with publications | |
| spa.io.aug | Data augmentation, e.g., generative models | Data aug with deep generative models. e.g., " variational autoencoders, generative adversarial networks, autoregressive models, KDE, normalizing flow models, energy-based models, and score-based models. " | ||
| spa.io.pre | Data preprocessing, e.g., window filter, x-binning, baseline removal. | Enhanced data preprocessing with novel window function in Raman spectroscopy: Leveraging feature selection and machine learning for raspberry origin identification [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2024. doi: 10.1016/j.saa.2024.124913 | ||
| spa.vis | Plotting | |||
| spa.cs | compressed sensing | cs1 | Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks [J]. Talanta, 2020, doi: 10.1016/j.talanta.2019.120681
Task-adaptive eigenvector-based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor [J]. Analytical Science Advances. Chemistry Europe, 2021, doi: 10.1002/ansa.202100018 Compressed Sensing library for spectroscopic profiling data [J]. Software Impacts, 2023, doi: 10.1016/j.simpa.2023.100492 Secured telemetry based on time-variant sensing matrix – An empirical study of spectroscopic profiling, Smart Agricultural Technology, Volume 5, 2023, doi: 10.1016/j.atech.2023.100268 Variational Auto-Encoder based Deep Compressed Sensing on Raman Spectroscopy [J]. Smart Agricultural Technology. 2025 |
|
| spa.fs | ||||
| spa.fs.nch_time_series_fs | channel alignment for e-nose; multi-channel e-nose/e-tongue data fs with 1d-laplacian conv kernel | 基于电子鼻和一维拉普拉斯卷积核的奶粉基粉产地鉴别,2024,doi: 10.13982/j.mfst.1673-9078.2024.5.0299 | ||
| spa.fs.glasso | Structured-fs of Raman data with group lasso | Cheese brand identification with Raman spectroscopy and sparse group LASSO [J], Journal of Food Composition and Analysis, 2025, doi: 10.1016/j.jfca.2025.107371 | ||
| spa.fs.mt | Multi-task feature selection for yogurt fermentation analysis | Studying yogurt fermentation dynamics using multi-task feature selection, 2025, 2nd-round review | ||
| spa.kernel | spa.kernel.* | Implementation of 31 atom kernel types | ackl | Analytical chemistry kernel library for spectroscopic profiling data, Food Chemistry Advances, Volume 3, 2023, 100342, ISSN 2772-753X, https://doi.org/10.1016/j.focha.2023.100342. |
| spa.kernel.mkl | Multi-kernel learning; PSO-MKL, GA-MKL | In progress | ||
| spa.dr | spa.dr.metrics | Dimensionality Reduction (DR) quality metrics | pyDRMetrics, wDRMetrics | pyDRMetrics - A Python toolkit for dimensionality reduction quality assessment, Heliyon, Volume 7, Issue 2, 2021, e06199, ISSN 2405-8440, doi: 10.1016/j.heliyon.2021.e06199. |
| spa.dr.mf | matrix-factorization based DR | pyMFDR | Matrix Factorization Based Dimensionality Reduction Algorithms - A Comparative Study on Spectroscopic Profiling Data [J], Analytical Chemistry, 2022. doi: 10.1021/acs.analchem.2c01922 | |
| spa.cla | spa.cla.metrics | classifiability analysis | pyCLAMs, wCLAMs | A unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8
pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, 2022, doi: 10.1016/j.softx.2022.101007 |
| spa.cla.ensemble | homo-stacking, hetero-stacking, FSSE | pyNNRW | Spectroscopic Profiling-based Geographic Herb Identification by Neural Network with Random Weights [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, doi: 10.1016/j.saa.2022.121348
Geographical origin identification of dendrobium officinale based on NNRW-stacking ensembles. Machine Learning with Applications [J]. 2024. doi: 10.1016/j.mlwa.2024.100594 |
|
| spa.cla.kernel | kernel-NNRW | |||
| spa.cla.nnrw | neural networks with random weights | |||
| spa.regress | Regression algorithms, e.g., GW-KNNR (Gaussian-weighted K-nearest neighbor regressor). | Quantification of Cow Milk in Adulterated Goat Milk Using Raman Spectroscopy and Machine Learning[J]. Microchemical Journal, 2025, doi: 10.1016/j.microc.2025.114319 | ||
| spa.pipeline | General data analysis pipelines. |
Building an Information Infrastructure of Spectroscopic Profiling Data for Food-Drug Quality and Safety Management [J]. Enterprise Information Systems, 2019, doi: 10.1080/17517575.2019
Machine learning-assisted MALDI-TOF MS toward rapid classification of milk products[J]. Journal of Dairy Science, 2024, doi:10.3168/jds.2024-24886 |
||
| spa.gui | Web-based apps. e.g., `python -m spa.gui.chaihu` will launch the app for bupleurum origin discrimination. | Rapid Raman Spectroscopy Analysis Assisted with Machine Learning: A Case Study on Radix Bupleuri[J], Journal of the Science of Food and Agriculture, 2024. doi:10.1002/jsfa.14012 | ||
Metadata
Release files for spa-ds 2.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| spa_ds-2.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Release files / spa_ds-2.2.0-py3-none-any.whl
| Download URL | spa_ds-2.2.0-py3-none-any.whl |
|---|---|
| Size | 15.3 MB |
| Tags | Python 3 |
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