cs1

Compressed Sensing library for 1D (one-dimensional) Spectroscopic Profiling Data

package	module	sub-module	description
cs1^[3]
	cs1.cs		basic functions for CS sensing, recovery, hyper-parameter grid-search, etc.
	cs1.basis	cs1.basis.common	commonly used non-adaptive CS transform bases^[1]
	cs1.basis	cs1.basis.adaptive	adaptive CS transform bases, e.g., LDA (linear discriminant analysis)-based, EBP (eigenvector-based projection)^[2], VAE (variational auto-encoder)
	cs1.metrics		CS-related metrics, e.g., mutual coherence, sparsity, MSE, KLD
	cs1.security	cs1.security.tvsm	time-variant sensing matrix mechanism^[4]
	cs1.domain	cs1.domain.audio	contains functions for audio and other one-dimensional signal processing. e.g., wave file I/O, lossy compression, ECG simulator
	cs1.domain	cs1.domain.image	contains functions for image processing. e.g., image CS, lossy compression
	cs1.gui		provides a web-based playground for researchers. users can try different CS bases and sampling ratios

Publications:
[1] Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks [J]. Talanta, 2020, doi: 10.1016/j.talanta.2019.120681
[2] 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
[3] Compressed Sensing library for spectroscopic profiling data [J]. Software Impacts, 2023, doi: 10.1016/j.simpa.2023.100492
[4] 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 [5] Variational Auto-Encoder based Deep Compressed Sensing on Raman Spectroscopy [J]. Smart Agricultural Technology. 2025

Installation

pip install cs1

A simple startup

import cs1

# Generate common non-adaptive bases and save to a local pickle file.
# The generation process can be very slow, so save it for future use.
cs1.basis.common.Generate_PSIs(n, savepath = 'PSIs_' + str(n) + '.pkl') # n is the data/signal dimensionality

# load back bases
file = open('PSIs_' + str(n) + '.pkl','rb')
PSIs = pickle.load(file)
file.close()

# sparsity analysis
cs1.metrics.analyze_sparsity(x, PSIs)

# compare different bases and sampling ratio on a single sample
mses, rmses = cs1.cs.GridSearch_Sensing_n_Recovery(x, PSIs, solver = 'LASSO') # returns MSEs and relative MSEs

low-level cs functions

from cs1.basis.common import *

dftmtx()
dctmtx()
hwtmtx()

from cs1.cs import *

sensing()
recovery()

from cs1.metrics import *

mutual_coherence()
...

singal processing functions for audio / image domains

from cs1.domain.audio import *

simulate_ECG()
dct_lossy_signal_compression()
dft_lossy_signal_compression()

from cs1.domain.image import *

img_dct()
img_dft()
dct_lossy_image_compression()
dft_lossy_image_compression()

adaptive cs bases

from cs1.basis.adaptive import *

PSI, _ = EBP(X) # X is a m-by-n training dataset. PSI is the EBP basis
PSI, _, _ = LDA(X, y, display = True) # X and y are training dataset. PSI is the LDA basis.

run as a local web server

python -m cs1.gui.run

You can then access the web GUI at the 5006 port:

Metadata

Release files for cs1 0.3.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for cs1 0.3.0
File	Interpreter	ABI	Platform	Reset
cs1-0.3.0-py3-none-any.whl 3.0 MB Apr 11, 2026	Python 3	none	any	Details

Release files / cs1-0.3.0-py3-none-any.whl

Download URL	cs1-0.3.0-py3-none-any.whl
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