Perform a gated counting measurement with the hardware. More...

Inheritance diagram for trace_analysis_logic.TraceAnalysisLogic:

Collaboration diagram for trace_analysis_logic.TraceAnalysisLogic:

Public Member Functions
def	__init__ (self, config, kwargs)
	Create CounterLogic object with connectors. More...

def	on_activate (self)
	Initialisation performed during activation of the module.

def	on_deactivate (self)
	Deinitialisation performed during deactivation of the module.

def	set_num_bins_histogram (self, num_bins, update=True)
	Set the number of bins. More...

def	do_calculate_histogram (self, mode='normal')
	Passes all the needed parameters to the appropriated methods. More...

def	calculate_histogram (self, trace, num_bins=None, custom_bin_arr=None)
	Calculate the histogram of a given trace. More...

def	analyze_flip_prob (self, trace, num_bins=None, threshold=None)
	General method, which analysis how often a value was changed from one data point to another in relation to a certain threshold. More...

def	analyze_flip_prob2 (self, trace, threshold=1, analyze_mode='full')
	General method, which analysis how often a value was changed from one data point to another in relation to a certain threshold. More...

def	analyze_flip_prob3 (self, trace, init_threshold=[1, ana_threshold=[1, analyze_mode='full')
	General method, which analysis how often a value was changed from one data point to another in relation to a certain threshold. More...

def	analyze_flip_prob4 (self, trace, bins=30, init_threshold=[1, ana_threshold=[1, analyze_mode='full')

def	analyze_flip_prob_postselect (self)
	Post select the data trace so that the flip probability is only calculated from a jump from below a threshold value to an value above threshold. More...

def	get_fit_functions (self)
	Return all fit functions, which are currently implemented for that module. More...

def	do_fit (self, fit_function=None)
	Makes the a fit of the current fit function. More...

def	do_no_fit (self)
	Perform no fit, basically return an empty array. More...

def	analyze_lifetime (self, trace, dt, method='postselect', distr='gaussian_normalized', state='\|-1 >', num_bins=50)
	Perform an lifetime analysis of a 1D time trace. More...

def	do_gaussian_fit (self, axis, data)
	Perform a gaussian fit. More...

def	do_doublegaussian_fit (self, axis, data)

def	do_doublepossonian_fit (self, axis, data)

def	do_possonian_fit (self, axis, data)

def	get_poissonian (self, x_val, mu, amplitude)
	Calculate, bases on the passed values a poisson distribution. More...

def	guess_threshold (self, hist_val=None, trace=None, max_ratio_value=0.1)
	Assume a distribution between two values and try to guess the threshold. More...

def	calculate_threshold (self, hist_data=None, distr='poissonian')
	Calculate the threshold by minimizing its overlap with the poissonian fits. More...

def	calculate_binary_trace (self, trace, threshold)
	Calculate for a given threshold all the trace values und output a binary array, where True = Below or equal Threshold False = Above Threshold. More...

def	extract_filtered_values (self, trace, threshold, below=True)
	Extract only those values, which are below or equal a certain Threshold. More...

Public Attributes
	hist_data

	spin_flip_prob

	fidelity_left

	fidelity_right

	trace

	current_fit_function

	debug_lifetime_x

	debug_lifetime_y

Static Public Attributes
	counterlogic1 = Connector(interface='CounterLogic')

	savelogic = Connector(interface='SaveLogic')

	fitlogic = Connector(interface='FitLogic')

	sigHistogramUpdated = QtCore.Signal()

	sigAnalysisResultsUpdated = QtCore.Signal()

Detailed Description

Perform a gated counting measurement with the hardware.

Constructor & Destructor Documentation

◆ init()

def trace_analysis_logic.TraceAnalysisLogic.__init__	(	self,
		config,
		kwargs
	)

Create CounterLogic object with connectors.

Parameters

dict	config: module configuration
dict	kwargs: optional parameters

Member Function Documentation

◆ analyze_flip_prob()

def trace_analysis_logic.TraceAnalysisLogic.analyze_flip_prob	(	self,
		trace,
		num_bins = `None`,
		threshold = `None`
	)

General method, which analysis how often a value was changed from one data point to another in relation to a certain threshold.

Parameters

np.array	trace: 1D trace of data
int	num_bins: optional, if a specific size for the histogram is desired, which is used to calculate the threshold.
float	threshold: optional, if a specific threshold is going to be used, otherwise the threshold is calculated from the data.

Returns: tuple(flip_prop, param): float the actual flip probability int the total number of flips float the fidelity float the calculated or passed threshold float the lifetime in the dark state in s float lifetime in the bright state in s

◆ analyze_flip_prob2()

def trace_analysis_logic.TraceAnalysisLogic.analyze_flip_prob2	(	self,
		trace,
		threshold = `1`,
		analyze_mode = `'full'`
	)

General method, which analysis how often a value was changed from one data point to another in relation to a certain threshold.

Parameters

np.array	trace: 1D trace of data
int	num_bins: optional, if a specific size for the histogram is desired, which is used to calculate the threshold.
float	threshold: optional, if a specific threshold is going to be used, otherwise the threshold is calculated from the data.

Returns: tuple(flip_prop, param): float the actual flip probability int the total number of flips float the fidelity float the calculated or passed threshold float the lifetime in the dark state in s float lifetime in the bright state in s

◆ analyze_flip_prob3()

def trace_analysis_logic.TraceAnalysisLogic.analyze_flip_prob3	(	self,
		trace,
		init_threshold = `[1`,
		ana_threshold = `[1`,
		analyze_mode = `'full'`
	)

General method, which analysis how often a value was changed from one data point to another in relation to a certain threshold.

Parameters

np.array	trace: 1D trace of data
float	threshold: optional, if a specific threshold is going to be used, otherwise the threshold is calculated from the data.

Returns: tuple(flip_prop, param): float the actual flip probability int the total number of flips float the fidelity float the calculated or passed threshold float the lifetime in the dark state in s float lifetime in the bright state in s

◆ analyze_flip_prob_postselect()

def trace_analysis_logic.TraceAnalysisLogic.analyze_flip_prob_postselect ( self )

Post select the data trace so that the flip probability is only calculated from a jump from below a threshold value to an value above threshold.

Returns: :

◆ analyze_lifetime()

def trace_analysis_logic.TraceAnalysisLogic.analyze_lifetime	(	self,
		trace,
		dt,
		method = `'postselect'`,
		distr = `'gaussian_normalized'`,
		state = `'\|-1>'`,
		num_bins = `50`
	)

Perform an lifetime analysis of a 1D time trace.

The analysis is based on the method provided ( for now only post select is implemented ).

Parameters

numpy	array trace: 1 D array
string	method: The method used for the lifetime analysis
string	distr: distribution used for analysis
string	state: State that the mw was applied to
int	num_bins: number of bins used in the histogram to determine the threshold before digitalisation of data

Returns: : dictionary containing the lifetimes of the different states |0>, |1>, |-1> in the case of the HMM method For the postselect method only lifetime for bright and darkstate is returned, keys are 'bright_state' and 'dark_state'

◆ calculate_binary_trace()

def trace_analysis_logic.TraceAnalysisLogic.calculate_binary_trace	(	self,
		trace,
		threshold
	)

Calculate for a given threshold all the trace values und output a binary array, where True = Below or equal Threshold False = Above Threshold.

Parameters

np.array	trace: a 1D array containing the y data, e.g. ccunts
float	threshold: value to decide whether a point in the trace is below/equal (True) or above threshold (False).

Returns: np.array: 1D trace of the length(trace) but now with boolean entries

◆ calculate_histogram()

def trace_analysis_logic.TraceAnalysisLogic.calculate_histogram	(	self,
		trace,
		num_bins = `None`,
		custom_bin_arr = `None`
	)

Calculate the histogram of a given trace.

Parameters

np.array	trace: a 1D trace
int	num_bins: number of bins between the minimal and maximal value of the trace. That must be an integer greater than or equal to 1.
np.array	custom_bin_arr: optional, 1D array. If a specific, non-uniform binning array is desired then it can be passed to the numpy routine. Then the parameter num_bins is ignored. Otherwise a uniform binning is applied by default.

Returns: : np.array: a 2D array, where first entry are the x_values and second entry are the count values. The length of the array is normally determined by the num_bins parameter. Usually the bins for the histogram are taken to be equally spaced, ranging from the minimal to the maximal value of the input trace array.

◆ calculate_threshold()

def trace_analysis_logic.TraceAnalysisLogic.calculate_threshold	(	self,
		hist_data = `None`,
		distr = `'poissonian'`
	)

Calculate the threshold by minimizing its overlap with the poissonian fits.

Parameters

np.array hist_data: 2D array which represent the x and y values of a histogram of a trace. string tells the function on what distribution it should calculate the threshold ( Added because it might happen that one normalizes data between (-1,1) and then a poissonian distribution won't work anymore.

Returns: tuple(float, float): threshold the calculated threshold between two overlapping poissonian distributed peaks. fidelity the measure how good the two peaks are resolved according to the calculated threshold The calculation of the threshold relies on fitting two poissonian distributions to the count histogram and minimize a threshold with respect to the overlap area:

◆ do_calculate_histogram()

def trace_analysis_logic.TraceAnalysisLogic.do_calculate_histogram	(	self,
		mode = `'normal'`
	)

Passes all the needed parameters to the appropriated methods.

Returns: :

◆ do_fit()

def trace_analysis_logic.TraceAnalysisLogic.do_fit	(	self,
		fit_function = `None`
	)

Makes the a fit of the current fit function.

Parameters

str	fit_function: name of the chosen fit function.

Returns: tuple(x_val, y_val, fit_results): x_val a 1D numpy array containing the x values y_val a 1D numpy array containing the y values fit_results a string containing the information of the fit results. You can obtain with get_fit_methods all implemented fit methods.

◆ do_gaussian_fit()

def trace_analysis_logic.TraceAnalysisLogic.do_gaussian_fit	(	self,
		axis,
		data
	)

Perform a gaussian fit.

Parameters

axis
data

Returns: :

◆ do_no_fit()

def trace_analysis_logic.TraceAnalysisLogic.do_no_fit ( self )

Perform no fit, basically return an empty array.

Returns: tuple(x_val, y_val, fit_results): x_val a 1D numpy array containing the x values y_val a 1D numpy array containing the y values fit_results a string containing the information of the fit results.

◆ extract_filtered_values()

def trace_analysis_logic.TraceAnalysisLogic.extract_filtered_values	(	self,
		trace,
		threshold,
		below = `True`
	)

Extract only those values, which are below or equal a certain Threshold.

Parameters

np.array	trace:
float	threshold:

Returns: tuple(index_array, filtered_array): np.array index_array: 1D integer array containing the indices of the passed trace array which are equal or below the threshold np.array filtered_array: the actual values of the trace, which are equal or below threshold

◆ get_fit_functions()

def trace_analysis_logic.TraceAnalysisLogic.get_fit_functions ( self )

Return all fit functions, which are currently implemented for that module.

Returns: list: with string entries denoting the name of the fit.

◆ get_poissonian()

def trace_analysis_logic.TraceAnalysisLogic.get_poissonian	(	self,
		x_val,
		mu,
		amplitude
	)

Calculate, bases on the passed values a poisson distribution.

Parameters

float	mu: expected value of poisson distribution
float	amplitude: Amplitude to which is multiplied on distribution
int,float	or np.array x_val: x values for poisson distribution, also works for numbers (int or float)

Returns: np.array: a 1D array with the calculated poisson distribution, corresponding to given parameters/ x values Calculate a Poisson distribution according to: P(k) = mu^k * exp(-mu) / k!

◆ guess_threshold()

def trace_analysis_logic.TraceAnalysisLogic.guess_threshold	(	self,
		hist_val = `None`,
		trace = `None`,
		max_ratio_value = `0.1`
	)

Assume a distribution between two values and try to guess the threshold.

Parameters

np.array	hist_val: 1D array which represent the y values of a histogram of a trace. Optional, if None is passed here, the passed trace will be used for calculations.
np.array	trace: optional, 1D array containing the y values of a meausured counter trace. If None is passed to hist_y_val then the threshold will be calculated from the trace.
float	max_ratio_value: the ratio how strong the lower y values will be cut off. For max_ratio_value=0.1 all the data which are 10% or less in amptitude compared to the maximal value are neglected. The guess procedure tries to find all values, which are max_ratio_value * maximum value of the histogram of the trace and selects those by indices. Then taking the first an the last might and assuming that the threshold is in the middle, gives a first estimate of the threshold value. FIXME That guessing procedure can be improved!

Returns: float: a guessed threshold

◆ set_num_bins_histogram()

def trace_analysis_logic.TraceAnalysisLogic.set_num_bins_histogram	(	self,
		num_bins,
		update = `True`
	)

Set the number of bins.

Parameters

int	num_bins: number of bins for the histogram
bool	update: if the change of bins should evoke a recalculation of the histogram.

The documentation for this class was generated from the following file:

logic/trace_analysis_logic.py

Public Member Functions

Public Attributes

Static Public Attributes

Detailed Description

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ analyze_flip_prob()

◆ analyze_flip_prob2()

◆ analyze_flip_prob3()

◆ analyze_flip_prob_postselect()

◆ analyze_lifetime()

◆ calculate_binary_trace()

◆ calculate_histogram()

◆ calculate_threshold()

◆ do_calculate_histogram()

◆ do_fit()

◆ do_gaussian_fit()

◆ do_no_fit()

◆ extract_filtered_values()

◆ get_fit_functions()

◆ get_poissonian()

◆ guess_threshold()

◆ set_num_bins_histogram()

◆ init()