quackseq.measurement

This module defines the measurement data structure and the fit class for measurement data.

Classes

Fit(name, domain, measurement)	The fit class for measurement data.
LorentzianFit(measurement[, name])	Lorentzian fit for measurement data.
Measurement(name, tdx, tdy, target_frequency)	This class defines how measurement data should look.
MeasurementError(name, error_meassage)	This will be returned if the measurement is not valid.
T2StarFit(measurement[, name])	T2* fit for measurement data.

class quackseq.measurement.Fit(name: str, domain: str, measurement: Measurement)

The fit class for measurement data. A fit can be performed on either the frequency or time domain data.

A measurement can have multiple fits.

fit() → None

Fits the measurement data and sets the fit parameters and covariance.

fit_function(x: array, *parameters) → array

The fit function.

Parameters:

• x (np.array) – The x data.

• *parameters – The fit parameters.

Returns:

The y data.

Return type:

np.array

classmethod from_json(data: dict, measurement: Measurement) → Fit

Converts the JSON format to a fit.

Parameters:

• data (dict) – The fit in JSON-compatible format.

• measurement (Measurement) – The measurement.

Returns:

The fit.

Return type:

Fit

initial_guess() → list

Initial guess for the fit.

Returns:

The initial guess.

Return type:

list

to_json() → dict

Converts the fit to a JSON-compatible format.

Returns:

The fit in JSON-compatible format.

Return type:

dict

property x: array

The x data of the fit.

property y: array

The y data of the fit.

class quackseq.measurement.LorentzianFit(measurement: Measurement, name: str = 'Lorentzian')

Lorentzian fit for measurement data.

fit() → None

Fits the measurement data and sets the fit parameters and covariance.

fit_function(f: array, S0: float, T2Star: float) → array

The Lorentzian fit function used for curve fitting.

initial_guess() → list

Initial guess for the Lorentzian fit.

class quackseq.measurement.Measurement(name: str, tdx: array, tdy: array, target_frequency: float, frequency_shift: float = 0, IF_frequency: float = 0)

This class defines how measurement data should look.

It includes pulse parameters necessary for further signal processing. Every spectrometer should adhere to this data structure in order to be compatible with the rest of the nqrduck.

Parameters:

• name (str) – Name of the measurement.

• tdx (np.array) – Time axis for the x axis of the measurement data.

• tdy (np.array) – Time axis for the y axis of the measurement data. This can be multi-dimensional. Axis 0 are the different data sets (e.g. phase cycles).

• target_frequency (float) – Target frequency of the measurement.

• frequency_shift (float, optional) – Frequency shift of the measurement. Defaults to 0.

• IF_frequency (float, optional) – Intermediate frequency of the measurement. Defaults to 0.

tdx

Time axis for the x axis of the measurement data. Axis 0 are the different data sets (e.g. phase cycles).

Type:

np.array

tdy

Time axis for the y axis of the measurement data. Axis 0 are the different data sets (e.g. phase cycles).

Type:

np.array

target_frequency

Target frequency of the measurement.

Type:

float

frequency_shift

Frequency shift of the measurement.

Type:

float

IF_frequency

Intermediate frequency of the measurement.

Type:

float

fdx

Frequency axis for the x axis of the measurement data.

Type:

np.array

fdy

Frequency axis for the y axis of the measurement data.

Type:

np.array

add_dataset(tdy: array) → None

Adds dataset to the measurement. We only add the y data, as the x data is the same for all datasets.

Parameters:

tdy (np.array) – Time axis for the y axis of the measurement data.

add_fit(fit: Fit) → None

Adds a fit to the measurement.

Parameters:

fit (Fit) – The fit to add.

apodization(function: Function) → Measurement

Applies apodization to the measurement data.

Parameters:

function (Function) – Apodization function.

Returns:

The apodized measurement.

Return type:

Measurement

delete_fit(fit: Fit) → None

Deletes a fit from the measurement.

Parameters:

fit (Fit) – The fit to delete.

edit_fit_name(fit: Fit, name: str) → None

Edits the name of a fit.

Parameters:

• fit (Fit) – The fit to edit.

• name (str) – The new name.

property fdx: array

Frequency domain data for the measurement (x).

property fdy: array

Frequency domain data for the measurement (y).

property fits: list

Fits of the measurement.

classmethod from_json(json: dict) → Measurement

Converts the JSON format to a measurement.

Parameters:

json (dict) – The measurement in JSON-compatible format.

Returns:

The measurement.

Return type:

Measurement

property name: str

Name of the measurement.

phase_shift(phase: float, index: int, axis=0) → array

Applies a phase shift to the measurement data.

Parameters:

• phase (float) – Phase shift in degrees.

• index (int) – Index of the data set to apply the phase shift to.

• axis (int) – Axis to apply the phase shift to. Defaults to 0.

serialize_complex_array(array: array) → list

Serializes a complex array to a list.

Parameters:

array (np.array) – The complex array.

Returns:

The serialized complex array.

Return type:

list

property target_frequency: float

Target frequency of the measurement.

property tdx: array

Time domain data for the measurement (x).

property tdy: array

Time domain data for the measurement (y).

to_json() → dict

Converts the measurement to a JSON-compatible format.

Returns:

The measurement in JSON-compatible format.

Return type:

dict

class quackseq.measurement.MeasurementError(name: str, error_meassage: str)

This will be returned if the measurement is not valid.

class quackseq.measurement.T2StarFit(measurement: Measurement, name: str = 'T2*')

T2* fit for measurement data.

fit() → None

Fits the measurement data and sets the fit parameters and covariance.

fit_function(t: array, S0: float, T2Star: float) → array

The T2* fit function used for curve fitting.

initial_guess() → list

Initial guess for the T2* fit.