Compressed Sensing library for 1D Spectroscopic Profiling Data
Project description
cs1
Compressed Sensing library for 1D Spectroscopic Profiling
Installation
pip install cs1
A simple startup
from cs1 import cs, adaptive
# 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_PSIHs(n, savepath = 'PSIHs_' + 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()
Project details
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