pygsti.extras.drift.signal.compute_auto_frequencies

pygsti.extras.drift.signal.compute_auto_frequencies#

compute_auto_frequencies(ds, transform='dct')#

Returns a set of frequencies to create spectra for, for the input data. These frequencies are in units of 1 / unit where unit is the units of the time-stamps in the DataSet. What this function is doing has a fundmentally different meaning depending on whether the transform is time-stamp aware (here, the LSP) or not (here, the DCT and DFT).

Time-stamp aware transforms take the frequencies to calculate powers at as an input, so this chooses these frequencies, which are, explicitly, the frequencies associated with the powers. The task of choosing the frequencies amounts to picking the best set of frequencies at which to interogate the true probability trajectory for components. As there are complex factors involved in this choice that the code has no way of knowing, sometimes it is best to choose them yourself. E.g., if different frequencies are used for different circuits it isn’t possible to (meaningfully) averaging power spectra across circuits, but this might be preferable if the time-step is sufficiently different between different circuits – it depends on your aims.

For time-stamp unaware transforms, these are the frequencies that, given that we’re implementing the, e.g., DCT, the generated power spectrum is implicitly with respect to. In the case of data on a fixed time-grid, i.e., equally spaced data, then there is a precise set of frequencies implicit in the transform. Otherwise, these frequencies are explicitly at least slightly ad hoc, and choosing these frequencies amounts to choosing those frequencies that “best” approximate the properties being interogatted with fitting each, e.g., DCT basis function to the (timestamp-free) data.

Parameters:

  • ds (DataSet or MultiDataset) – Contains time-series data that the “auto” frequencies are calculated for.

  • transform (str, optional) – The type of transform that the frequencies are being chosen for.

Returns:

  • frequencies (list) – A list of lists, where each of the consituent lists is a set of frequencies that are the are the derived frequencies for one or more of the dataset rows (i.e., for one or circuits). It is common for this to be a length-1 list, containing a single set of frequencies that are the frequencies this function has chosen for every circuit.

  • pointers (dict) – A dictionary that “points” from the index of a circuit (indexed in the order of ds.keys()) to the index of the list of frequencies for that circuit. No entry in the dictionary for an index should be interpretted as 0. So this can be an empty dictionary (with the frequencies list then necessarily of length 1, containing a single set of frequencies for every circuit).