cs1 0.0.9

Compressed Sensing library for 1D Spectroscopic Profiling Data

cs1

Compressed Sensing library for 1D Spectroscopic Profiling

Installation

pip install cs1

A simple startup

from cs1.cs import *

# 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.
cs.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
Analyze_Sparsity(x, PSIs)

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

low-level cs functions

dftmtx()
dctmtx()
hwtmtx()
Sensing()
Recovery()
Mutual_Coherence()
...

singal processing functions for other domains

Simulate_ECG()
dct_lossy_signal_compression()
dft_lossy_signal_compression()
img_dct()
img_dft()
dct_lossy_image_compression()
dft_lossy_image_compression()

adaptive cs bases

from cs1.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.