pygsti.report.focused_mc2gst_gateset¶
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pygsti.report.
focused_mc2gst_gateset
(gateStringToEstimate, gateStringLabel, dataset, specs, startGateset, minProbClipForWeighting=0.0001, probClipInterval=(-1000000.0, 1000000.0), verbosity=0)¶ Constructs a gateset containing a single LSGST estimate of gateStringToEstimate.
Starting with startGateset, run LSGST with the same gate strings that LGST would use to estimate gateStringToEstimate. That is, LSGST is run with strings of the form: prepStr + gateStringToEstimate + effectStr and return the resulting Gateset.
Parameters: - gateStringToEstimate (GateString or tuple) – The single gate string to estimate using LSGST
- gateStringLabel (string) – The label for the estimate of gateStringToEstimate. i.e. gate_matrix = returned_gateset[gate_label]
- dataset (DataSet) – The data to use for LGST
- specs (2-tuple) – A (prepSpecs,effectSpecs) tuple usually generated by calling build_spam_specs(...)
- startGateset (GateSet) – The gate set to seed LSGST with. Often times obtained via LGST.
- minProbClipForWeighting (float, optional) – defines the clipping interval for the statistical weight used within the chi^2 function (see chi2fn).
- probClipInterval (2-tuple, optional) – (min,max) to clip probabilities to within GateSet probability computation routines (see GateSet.bulk_fill_probs)
- verbosity (int, optional) – Verbosity value to send do_mc2gst(...) call.
Returns: A gateset containing LSGST estimate of gateStringToEstimate.
Return type: Gateset