Analysis#

The analysis subpackage contains objects to process and manipulate experimental data.

`keysight.qcs.analysis.BaseFitter`	A class for fitting real-valued data.
`keysight.qcs.analysis.ComplexFitter`	A class for fitting complex-valued data.
`keysight.qcs.analysis.ComplexLorentzian`	A fitter for a complex Lorentzian.
`keysight.qcs.analysis.DecayingSinusoid`	A fitter for a decaying sinusoid.
`keysight.qcs.analysis.Estimate`	Class that stores fit information returned from data analysis.
`keysight.qcs.analysis.EstimateCollection`	An iterable container of estimates with several convenience functions which make it easier to select specific estimates.
`keysight.qcs.analysis.EstimateTuple`	A namedtuple container for storing a single estimate name, value, and standard deviation.
`keysight.qcs.analysis.Exponential`	A fitter for an exponential curve.
`keysight.qcs.analysis.FanoResonance`	A fitter for a Fano resonance with an added Gaussian dip, to capture sharp resonator response features with an asymmetric background.
`keysight.qcs.analysis.IQuadrature`	Extracts the I quadrature from complex IQ data.
`keysight.qcs.analysis.LinearDetrend`	Removes linear trends from experimental data.
`keysight.qcs.analysis.Preprocessor`	A class that processes experimental data before fitting.

Estimates#

class keysight.qcs.analysis.Estimate(names: list[str], values: ndarray, cov: ndarray)#

Bases: Serializable

Class that stores fit information returned from data analysis.

Parameters:

names – The names of the parameters estimated by the fit.
values – The values of the estimates.
cov – The covariance matrix of the estimates.

property cov: ndarray#: The covariance matrix of this estimate.

property names: list[str]#: A list of strings which describe the parameters returned from fitting.

property std: ndarray#: A vector of standard deviations for every element of this estimate.

property values: ndarray#: A vector containing the estimated values associated with the provided names.

draw(filename: str = 'qcs_estimate.html', display: bool = True) → None#

Creates an HTML representation of this Estimate and saves it to a file.

Parameters:

filename – The filename to write the HTML to (if executing in a script)
display – Whether to display the HTML. When set to False, this will only save the file to the specified filename.

class keysight.qcs.analysis.EstimateCollection(estimates: Estimate | Iterable[Estimate] | None = None, type: str = '1D')#

Bases: Serializable

An iterable container of estimates with several convenience functions which make it easier to select specific estimates.

Parameters:: estimates – Either a single estimate or an iterable of estimates to include in the collection.

property estimates: list[Estimate]#: The estimates in this collection.

property type: str#: The type of the estimate collection.

append(estimate: Estimate | Iterable[Estimate])#

Appends a single estimate or an iterable of estimates to the collection.

Parameters:: estimate – A single estimate or an iterable of estimates.

vector(name: str) → ndarray#

Constructs a vector of estimates for a given parameter.

Parameters:: name – The name of the parameter to extract.

draw(filename: str = 'qcs_estimate_collection.html', display: bool = True) → None#

Creates an HTML representation of this estimate collection and saves it to a file.

Parameters:

filename – The filename to write the HTML to (if executing in a script)
display – Whether to display the HTML. When set to False, this will only save the file to the specified filename.

class keysight.qcs.analysis.EstimateTuple(name, val, std)#

Bases: tuple

A namedtuple container for storing a single estimate name, value, and standard deviation.

Parameters:

name – The name of the estimate.
val – The value of the estimate.
std – The standard deviation of the estimate.

Fitting#

class keysight.qcs.analysis.BaseFitter#

Bases: Serializable

A class for fitting real-valued data.

abstract property model: Callable#: The model. Must have ‘x’ (corresponding to the x-axis data) as first parameter for displaying the model equation in interactive plot.

abstract property names: tuple[str, ...]#: The names of the parameters of the model. Must match the names used in the model for interactive plot.

apply_fitter(x_values: ndarray, y_values: ndarray, guess: tuple[float, ...] | None = None, preprocessors: Iterable[Preprocessor] | None = None, qudit_index: int | None = None, bounds: tuple[Sequence[float], Sequence[float]] | Bounds | None = None, method: Literal['lm', 'trf', 'dogbox'] | None = None, loss: Literal['linear', 'soft_l1', 'huber', 'cauchy', 'arctan'] | Callable[[ndarray], ndarray] | None = 'linear') → Estimate#

Performs nonlinear least-squares fitting of a model to the input data.

Uses scipy.optimize.curve_fit(). For details on least-squares fitting, see: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html

Parameters:

x_values – The independent variable values.
y_values – The data to fit.
guess – The initial guess for the parameters. If None, uses the make_guess method.
preprocessors – An iterable of Preprocessors that are applied to the data before fitting.
qudit_index – Optional index to look up a saved guess.
bounds – Lower and upper bounds on model parameters. If not given, defaults to unconstrained fitting.
method – Optimization algorithm to use. Sets ‘lm’ by default but if bounds are given, switches to method=’trf’ (or ‘dogbox’, if specified.)
loss – Loss function to use in robust fitting (for ‘trf’ and ‘dogbox’). Either a string with method name or custom callable that maps residuals to loss values.

Returns:

Estimate for the parameters of the fitting model.

abstract make_guess(x_values: ndarray, y_values: ndarray) → tuple[float, ...]#

Generates a guess for the parameters.

Parameters:

x_values – The independent variable values.
y_values – The data to fit.

class keysight.qcs.analysis.ComplexFitter#

Bases: BaseFitter

A class for fitting complex-valued data.

apply_fitter(x_values: ndarray, y_values: ndarray, guess=None, preprocessors=None, qudit_index: int | None = None, bounds: tuple[Sequence[float], Sequence[float]] | Bounds | None = None, method: Literal['lm', 'trf', 'dogbox'] | None = None, loss: Literal['linear', 'soft_l1', 'huber', 'cauchy', 'arctan'] | Callable[[ndarray], ndarray] | None = 'linear') → Estimate#

Performs nonlinear least-squares fitting of a model to the input data.

Uses scipy.optimize.curve_fit(). For details on least-squares fitting, see: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html

Parameters:

x_values – The independent variable values.
y_values – The data to fit.
guess – The initial guess for the parameters. If None, uses the make_guess method.
preprocessors – An iterable of Preprocessors that are applied to the data before fitting.
qudit_index – Optional index to look up a saved guess.
bounds – Lower and upper bounds on model parameters. If not given, defaults to unconstrained fitting.
method – Optimization algorithm to use. Sets ‘lm’ by default but if bounds are given, switches to method=’trf’ (or ‘dogbox’, if specified.)
loss – Loss function to use in robust fitting (for ‘trf’ and ‘dogbox’).

Returns:

Estimate for the parameters of the fitting model.

class keysight.qcs.analysis.ComplexLorentzian#

Bases: ComplexFitter

A fitter for a complex Lorentzian.

property model#: The model. Must have ‘x’ (corresponding to the x-axis data) as first parameter for displaying the model equation in interactive plot.

property names#: The names of the parameters of the model. Must match the names used in the model for interactive plot.

make_guess(x_values: ndarray, y_values: ndarray) → tuple#

Generates a guess for the parameters.

Parameters:

x_values – The independent variable values.
y_values – The data to fit.

class keysight.qcs.analysis.DecayingSinusoid#

Bases: BaseFitter

A fitter for a decaying sinusoid.

property model#: The model. Must have ‘x’ (corresponding to the x-axis data) as first parameter for displaying the model equation in interactive plot.

property names#: The names of the parameters of the model. Must match the names used in the model for interactive plot.

make_guess(x_values, y_values) → tuple#

Generates a guess for the parameters.

Parameters:

x_values – The independent variable values.
y_values – The data to fit.

class keysight.qcs.analysis.Exponential#

Bases: BaseFitter

A fitter for an exponential curve.

property model#: The model. Must have ‘x’ (corresponding to the x-axis data) as first parameter for displaying the model equation in interactive plot.

property names#: The names of the parameters of the model. Must match the names used in the model for interactive plot.

make_guess(x_values, y_values)#

Generates a guess for the parameters.

Parameters:

x_values – The independent variable values.
y_values – The data to fit.

class keysight.qcs.analysis.FanoResonance#

Bases: ComplexFitter

A fitter for a Fano resonance with an added Gaussian dip, to capture sharp resonator response features with an asymmetric background.

\[\begin{equation*} A\times((q + ε)^2/(1 + ε^2))+ B-C\times\exp{(-(x - x_0)^2/(2 \times \sigma^2))} \end{equation*}\]

where \(ε = (x - x_0) / (\gamma / 2)\)

property model#: The model. Must have ‘x’ (corresponding to the x-axis data) as first parameter for displaying the model equation in interactive plot.

property names#

The names of the parameters of the model.

Returns:

q: Fano asymmetry parameter. Controls the skewness of the resonance.
gamma: Full width at half maximum (FWHM) of the resonance.
x0: Resonance frequency.
A: Amplitude (depth or height) of the Fano profile.
offset: Vertical offset added to the Fano profile.
amp_dip: Amplitude of the Gaussian dip subtracted from the Fano curve.
sigma_dip: Standard deviation of the Gaussian dip.

make_guess(x_values: ndarray, y_values: ndarray)#

Generates a guess for the parameters.

Parameters:

x_values – The independent variable values.
y_values – The data to fit.

apply_fitter(x_values: ndarray, y_values: ndarray, guess: tuple[float, float, float, float, float, float, float] | None = None, qudit_index: int | None = None) → Estimate#

Fits an array and returns the fitted parameters.

Parameters:

x_values – The independent variable values.
y_values – The data to fit.
guess – The initial guess for the parameters. If None, uses the make_guess method.
qudit_index – Optional index to look up a saved guess.

Returns:

Estimate for the parameters of the fitting model.

Preprocessing#

class keysight.qcs.analysis.Preprocessor#

Bases: Serializable

A class that processes experimental data before fitting.

abstract process(x_values: ndarray, y_values: ndarray) → tuple[ndarray, ndarray]#: Process the data.

class keysight.qcs.analysis.LinearDetrend#

Bases: Preprocessor

Removes linear trends from experimental data.

process(x_values, y_values)#: Process the data.

class keysight.qcs.analysis.IQuadrature#

Bases: Preprocessor

Extracts the I quadrature from complex IQ data.

process(x_values, y_values)#: Process the data.