pygsti.extras.drift.signal

Signal analysis functions for time-series data

Functions

amplitudes_at_frequencies(freq_indices, ...)	Finds the amplitudes in the data at the specified frequency indices.
bartlett_spectrum(x, numspectra[, counts, ...])	Calculates the Bartlett power spectrum.
compute_auto_frequencies(ds[, transform])	Returns a set of frequencies to create spectra for, for the input data.
dct(x[, null_hypothesis, counts])	Returns the Type-II discrete cosine transform of y, with an orthogonal normalization, where
dct_basisfunction(omega, times, starttime, ...)	The omega`th DCT basis function, for a initial time of `starttime and a time-step of timedif, evaluated at the times times.
dft(x[, null_hypothesis, counts])	Returns the discrete Fourier transform of y, with a unitary normalization, where y is an array with elements related to the x array by
fourier_frequencies_from_times(times)	Calculates the Fourier frequencies from a set of times.
frequencies_from_timestep(timestep, numtimes)	Calculates the Fourier frequencies associated with a timestep and a total number of times.
generate_flat_signal(power, nummodes, n[, ...])	Generates a minimal sparsity probability trajectory for the specified power, and a specified number of modes containing non-zero power.
generate_gaussian_signal(power, center, ...)	Generates a probability trajectory with the specified power that is approximately Gaussian distribution accross frequencies, centered in the frequency specified by center.
idct(modes, null_hypothesis[, counts])	Inverts the dct function.
idft(modes, null_hypothesis[, counts])	Inverts the dft function.
logistic_transform(x, mean)	Transforms the input float x as:
lowpass_filter(data[, max_freq])	Implements a low-pass filter on the input array, by DCTing the input, mapping all but the lowest max_freq modes to zero, and then inverting the transform.
lsp(x, times[, frequencies, ...])	Performs a Lomb-Scargle periodogram (lsp) on the input data, returning powers and frequencies.
maxpower_pvalue(maxpower, numpowers, dof)	The p-value of the test statistic max(lambda_i) where there are numpowers lambda_i test statistics, and they are i.i.d.
moving_average(sequence[, width])	Implements a moving average on sequence with an averaging width of width.
power_significance_quasithreshold(...[, ...])	The Benjamini-Hockberg quasi-threshold for finding the statistically significant powers in a power spectrum.
power_significance_threshold(significance, ...)	The multi-test adjusted signficance statistical significance threshold for testing numtests test statistics that all have a marginal distribution that is chi2 with dof degrees of freedom.
power_to_pvalue(power, dof)	Converts a power to a p-value, under the assumption that the power is chi2 distribution with dof degrees of freedom.
renormalizer(p[, method])	Takes an arbitrary input vector p and maps it to a vector bounded within [0,1].
sparsity(p)	Returns the Hoyer sparsity index of the input vector p.
spectrum(x[, times, null_hypothesis, ...])	Generates a power spectrum from the input time-series data.
standardizer(x[, null_hypothesis, counts])	Maps the vector x over [0, counts] as
unstandardizer(z, null_hypothesis[, counts])	Inverts the standardizer function.