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A brief update on results, which suggest 60 random samples may not be the right number to reliably reconstruct a 512-length signal with 30 nonzero values. Would appreciate any feedback. Looped through the above code over the available solvers in cvxpy, 50 trials per solver, and produced the following average and median error rates (i.e. number of vals not ...


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Well it's not just the gaps; your data is also non-uniformly sampled. Use index_col to use the time column as the index to your dataframe: df = pd.read_table('BD-10d4669.p.1', sep=' ', engine='python', names=['time', 'mag'], index_col=0) df.plot(y='mag'); mag time ...


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OP's time vector is What I'd do: Treat it as piecewise-lienar, i.e. ignore that the time vector isn't uniformly spaced except for jumps. This should work reasonably - but if greater accuracy is desired, there's a related inquiry. Define "jump" threshold that separates each "segment" Pad each such segment from each side - that is, have ...


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noverlap = nperseg - 1 provides maximum possible information - it is the 'ideal' configuration. A spectrogram is $|\text{STFT}|$, and $\text{STFT}$ is input convolved with windowed complex sinusoids. noverlap is surrogate for hop_size: hop_size == nperseg - noverlap hopsize is the stride of convolution But if I increase it too much, I get nothing, the plot ...


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